Identification, monitoring and treatment of disease and characterization of biological condition using gene expression profiles

ABSTRACT

A method provides an index that is indicative of the state of a subject, as to a biological condition, based on a sample from the subject. An embodiment of this method includes: deriving from the sample a profile data set, the profile data set including a plurality of members, each member being a quantitative measure of the amount of a distinct RNA or protein constituent in a panel of constituents selected so that measurement of the constituents enables evaluation of the biological condition; and in deriving the profile data set, achieving such measure for each constituent under measurement conditions that are substantially repeatable; and applying values from the profile data set to an index function that provides a mapping from an instance of a profile data set into a single-valued measure of biological condition, so as to produce an index pertinent to the biological condition of the subject.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.12/609,578, filed Oct. 30, 2009; which is a continuation of U.S.application Ser. No. 11/158,504, filed Jun. 22, 2005; which is acontinuation of U.S. application Ser. No. 10/291,856, filed Nov. 8,2002; which claims priority to U.S. provisional application No.60/348,213, filed Nov. 9, 2001; U.S. provisional application No.60/340,881, filed Dec. 7, 2001; U.S. provisional application No.60/369,633, filed Apr. 3, 2002; and U.S. provisional application No.60/376,997, filed Apr. 30, 2002; which disclosures are hereinincorporated by reference in their entirety.

TECHNICAL FIELD AND BACKGROUND ART

The present invention relates to use of gene expression data, and inparticular to use of gene expression data in identification, monitoringand treatment of disease and in characterization of biological conditionof a subject.

The prior art has utilized gene expression data to determine thepresence or absence of particular markers as diagnostic of a particularcondition, and in some circumstances have described the cumulativeaddition of scores for over expression of particular disease markers toachieve increased accuracy or sensitivity of diagnosis. Information onany condition of a particular patient and a patient's response to typesand dosages of therapeutic or nutritional agents has become an importantissue in clinical medicine today not only from the aspect of efficiencyof medical practice for the health care industry but for improvedoutcomes and benefits for the patients.

SUMMARY OF THE INVENTION

In a first embodiment, there is provided a method, for evaluating abiological condition of a subject, based on a sample from the subject.The method includes: deriving from the sample a profile data set, theprofile data set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or

protein constituent in a panel of constituents selected so thatmeasurement of the constituents enables evaluation of the biologicalcondition; and

in deriving the profile data set, achieving such measure for eachconstituent under measurement conditions that are substantiallyrepeatable.

There is a related embodiment for providing an index that is indicativeof the state of a subject, as to a biological condition, based on asample from the subject. This embodiment includes:

deriving from the sample a profile data set, the profile data setincluding a plurality of members, each member being a quantitativemeasure of the amount of a distinct RNA or protein constituent in apanel of constituents selected so that measurement of the constituentsenables evaluation of the biological condition; and

in deriving the profile data set, achieving such measure for eachconstituent under measurement conditions that are substantiallyrepeatable; and

applying values from the profile data set to an index function thatprovides a mapping from an instance of a profile data set into asingle-valued measure of biological condition, so as to produce an indexpertinent to the biological condition of the subject.

In further embodiments related to the foregoing, there is also included,in deriving the profile data set, achieving such measure for eachconstituent under measurement conditions wherein specificity andefficiencies of amplification for all constituents are substantiallysimilar. Similarly further embodiments include alternatively or inaddition, in deriving the profile data set, achieving such measure foreach constituent under measurement conditions wherein specificity andefficiencies of amplification for all constituents are substantiallysimilar.

In embodiments relating to providing the index a further embodiment alsoincludes providing with the index a normative value of the indexfunction, determined with respect to a relevant population, so that theindex may be interpreted in relation to the normative value. Optionallyproviding the normative value includes constructing the index functionso that the normative value is approximately 1. Also optionally, therelevant population has in common a property that is at least one of agegroup, gender, ethnicity, geographic location, diet, medical disorder,clinical indicator, medication, physical activity, body mass, andenvironmental exposure.

In another related embodiment, efficiencies of amplification, expressedas a percent, for all constituents lie within a range of approximately 2percent, and optionally, approximately 1 percent.

In another related embodiment, measurement conditions are repeatable sothat such measure for each constituent has a coefficient of variation,on repeated derivation of such measure from the sample, that is lessthan approximately 3 percent. In further embodiments, the panel includesat least three constituents and optionally fewer than approximately 500constituents.

In another embodiment, the biological condition being evaluated is withrespect to a localized tissue of the subject and the sample is derivedfrom tissue or fluid of a type distinct from that of the localizedtissue.

In related embodiments, the biological condition may be any of theconditions identified in Tables 1 through 12 herein, in which case thereare measurements conducted corresponding to constituents of thecorresponding Gene Expression Panel. The panel in each case includes atleast two, and optionally at least three, four, five, six, seven, eight,nine or ten, of the constituents of the corresponding Gene ExpressionPanel.

In another embodiment, there is provided a method of providing an indexthat is indicative of the inflammatory state of a subject based on asample from the subject that includes: deriving from the sample a firstprofile data set, the first profile data set including a plurality ofmembers, each member being a quantitative measure of the amount of adistinct RNA or protein constituent in a panel of constituents, thepanel including at least two of the constituents of the InflammationGene Expression Panel of Table 1; (although in other embodiments, atleast three, four, five, six or ten constituents of the panel of Table 1may be used in a panel) wherein, in deriving the first profile data set,such measure is performed for each constituent both under conditionswherein specificity and efficiencies of amplification for allconstituents are substantially similar and under substantiallyrepeatable conditions; and applying values from the first profile dataset to an index function that provides a mapping from an instance of aprofile data set into a single-valued measure of biological condition(in an embodiment, this may be an inflammatory condition), so as toproduce an index pertinent to the biological condition of the sample orthe subject. The biological condition may be any condition that isassessable using an appropriate Gene Expression Panel; the measurementof the extent of inflammation using the Inflammation Gene ExpressionPanel is merely an example.

In additional embodiments, the mapping by the index function may befurther based on an instance of a relevant baseline profile data set andvalues may be applied from a corresponding baseline profile data setfrom the same subject or from a population of subjects or samples with asimilar or different biological condition. Additionally, the indexfunction may be constructed to deviate from a normative value generallyupwardly in an instance of an increase in expression of a constituentwhose increase is associated with an increase of inflammation and alsoin an instance of a decrease in expression of a constituent whosedecrease is associated with an increase of inflammation. The indexfunction alternatively be constructed to weigh the expression value of aconstituent in the panel generally in accordance with the extent towhich its expression level is determined to be correlated with extent ofinflammation. The index function may be alternatively constructed totake into account clinical insight into inflammation biology or to takeinto account experimentally derived data or to take into accountrelationships derived from computer analysis of profile data sets in adata base associating profile data sets with clinical and demographicdata. In this connection, the construction of the index function may beachieved using statistical methods, which evaluate such data, toestablish a model of constituent expression values that is an optimizedpredictor of extent of inflammation.

In another embodiment, the panel includes at least one constituent thatis associated with a specific inflammatory disease.

The methods described above may further utilize the step wherein (i) themapping by the index function is also based on an instance of at leastone of demographic data and clinical data and (ii) values are appliedfrom the first profile data set including applying a set of valuesassociated with at least one of demographic data and clinical data.

In another embodiment of the above methods, a portion of deriving thefirst profile data set is performed at a first location and applying thevalues from the first profile data set is performed at a secondlocation, and data associated with performing the portion of derivingthe first profile data set are communicated to the second location overa network to enable, at the second location, applying the values fromthe first profile data set.

In an embodiment of the methods, the index function is a linear sum ofterms, each term being a contribution function of a member of theprofile data set. Moreover, the contribution function may be a weightedsum of powers of one of the member or its reciprocal, and the powers maybe integral, so that the contribution function is a polynomial of one ofthe member or its reciprocal. Optionally, the polynomial is a linearpolynomial. The profile data set may include at least three, four or allmembers corresponding to constituents selected from the group consistingof IL1A, IL1B, TNF, IFNG and IL10. The index function may beproportional to ¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10} and bracesaround a constituent designate measurement of such constituent.

In an additional embodiment, a method is provided of analyzing complexdata associated with a sample from a subject for information pertinentto inflammation, the method that includes: deriving a Gene ExpressionProfile for the sample, the Gene Expression Profile being based on aSignature Panel for Inflammation; and using the Gene Expression Profileto determine a Gene Expression Profile Inflammatory Index for thesample.

In an additional embodiment, a method is provided of monitoring thebiological condition of a subject, that includes deriving a GeneExpression Profile for each of a series of samples over time from thesubject, the Gene Expression Profile being based on a Signature Panelfor Inflammation; and for each of the series of samples, using thecorresponding Gene Expression Profile to determine a Gene ExpressionProfile Inflammatory Index.

In an additional embodiment, there is provided a method of determiningat least one of (i) an effective dose of an agent to be administered toa subject and (ii) a schedule for administration of an agent to asubject, the method including: deriving a Gene Expression Profile for asample from the subject, the Gene Expression Profile being based on aSignature Panel for Inflammation; using the Gene Expression Profile todetermine a Gene Expression Profile Inflammatory Index for the sample;and

using the Gene Expression Profile Inflammatory Index as an indicator inestablishing at least one of the effective dose and the schedule.

In an additional embodiment, a method of guiding a decision to continueor modify therapy for a biological condition of a subject, is providedthat includes: deriving a Gene Expression Profile for a sample from thesubject, the Gene Expression Profile being based on a Signature Panelfor Inflammation; and using the Gene Expression Profile to determine aGene Expression Profile Inflammatory Index for the sample.

A method of predicting change in biological condition of a subject as aresult of exposure to an agent, is provided that includes: deriving afirst Gene Expression Profile for a first sample from the subject in theabsence of the agent, the first Gene Expression Profile being based on aSignature Panel for Inflammation; deriving a second Gene ExpressionProfile for a second sample from the subject in the presence of theagent, the second Gene Expression Profile being based on the sameSignature Panel; and using the first and second Gene Expression Profilesto determine correspondingly a first Gene Expression ProfileInflammatory Index and a second Gene Expression Profile InflammatoryIndex. Accordingly, the agent may be a compound and the compound may betherapeutic.

In an additional embodiment, a method of evaluating a property of anagent is provided where the property is at least one of purity, potency,quality, efficacy or safety, the method including: deriving a first GeneExpression Profile from a sample reflecting exposure to the agent of (i)the sample, or (ii) a population of cells from which the sample isderived, or (iii) a subject from which the sample is derived; using theGene Expression Profile to determine a Gene Expression ProfileInflammatory Index; and using the Gene Expression Profile InflammatoryIndex in determining the property.

In accordance with another embodiment there is provided a method ofproviding an index that is indicative of the biological state of asubject based on a sample from the subject.

The method of this embodiment includes:

deriving from the sample a first profile data set, the first profiledata set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents, the panel including at least twoof the constituents of the Inflammation Gene Expression Panel of Table1; and

applying values from the first profile data set to an index functionthat provides a mapping from an instance of a profile data set into asingle-valued measure of biological condition, so as to produce an indexpertinent to the biological condition of the sample or the subject.

In carrying out this method the index function also uses data from abaseline profile data set for the panel. Each member of the baselinedata set is a normative measure, determined with respect to a relevantpopulation of subjects, of the amount of one of the constituents in thepanel. In addition, in deriving the first profile data set and thebaseline data set, such measure is performed for each constituent bothunder conditions wherein specificity and efficiencies of amplificationfor all constituents are substantially similar and under substantiallyrepeatable conditions.

In another type of embodiment, there is provided a method, forevaluating a biological condition of a subject, based on a sample fromthe subject. In this embodiment, the method includes:

deriving from the sample a first profile data set, the first profiledata set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents selected so that measurement ofthe constituents enables measurement of the biological condition; and

producing a calibrated profile data set for the panel, wherein eachmember of the calibrated profile data set is a function of acorresponding member of the first profile data set and a correspondingmember of a baseline profile data set for the panel.

In this embodiment, each member of the baseline data set is a normativemeasure, determined with respect to a relevant population of subjects,of the amount of one of the constituents in the panel, and thecalibrated profile data set provides a measure of the biologicalcondition of the subject.

In a similar type of embodiment, there is provided a method, forevaluating a biological condition of a subject, based on a sample fromthe subject, and the method of this embodiment includes:

applying the first sample or a portion thereof to a defined populationof indicator cells;

obtaining from the indicator cells a second sample containing at leastone of RNAs or proteins;

deriving from the second sample a first profile data set, the firstprofile data set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents selected so that measurement ofthe constituents enables measurement of the biological condition; and

producing a calibrated profile data set for the panel, wherein eachmember of the calibrated profile data set is a function of acorresponding member of the first profile data set and a correspondingmember of a baseline profile data set for the panel, wherein each memberof the baseline data set is a normative measure, determined with respectto a relevant population of subjects, of the amount of one of theconstituents in the panel, the calibrated profile data set providing ameasure of the biological condition of the subject.

Furthermore, another and similar, type of embodiment provides a method,for evaluating a biological condition affected by an agent. The methodof this embodiment includes:

obtaining, from a target population of cells to which the agent has beenadministered, a sample having at least one of RNAs and proteins;

deriving from the sample a first profile data set, the first profiledata set including a plurality of members, each member being aquantitative measure of the amount of a distinct RNA or proteinconstituent in a panel of constituents selected so that measurement ofthe constituents enables measurement of the biological condition; and

producing a calibrated profile data set for the panel, wherein eachmember of the calibrated profile data set is a function of acorresponding member of the first profile data set and a correspondingmember of a baseline profile data set for the panel, wherein each memberof the baseline data set is a normative measure, determined with respectto a relevant population of subjects, of the amount of one of theconstituents in the panel, the calibrated profile data set providing ameasure of the biological condition as affected by the agent.

In further embodiments based on these last three embodiments, therelevant population may be a population of healthy subjects.Alternatively, or in addition, the relevant population has in common aproperty that is at least one of age group, gender, ethnicity,geographic location, diet, medical disorder, clinical indicator,medication, physical activity, body mass, and environmental exposure.

Alternatively or in addition, the panel includes at least two of theconstituents of the Inflammation Gene Expression Panel of Table 1.(Other embodiments employ at least three, four, five, six, or ten ofsuch constituents.) Also alternatively or in addition, in deriving thefirst profile data set, such measure is performed for each constituentboth under conditions wherein specificity and efficiencies ofamplification for all constituents are substantially similar and undersubstantially repeatable conditions. Also alternatively, when suchmeasure is performed for each constituent both under conditions whereinspecificity and efficiencies of amplification for all constituents aresubstantially similar and under substantially repeatable conditions,optionally one need not produce a calibrated profile data set, but mayinstead work directly with the first data set.

In another embodiment, there is provided a method, for evaluating theeffect on a biological condition by a first agent in relation to theeffect by a second agent. The method of this embodiment includes:

obtaining, from first and second target populations of cells to whichthe first and second agents have been respectively administered, firstand second samples respectively, each sample having at least one of RNAsand proteins;

deriving from the first sample a first profile data set and from thesecond sample a second profile data set, the profile data sets eachincluding a plurality of members, each member being a quantitativemeasure of the amount of a distinct RNA or protein constituent in apanel of constituents selected so that measurement of the constituentsenables measurement of the biological condition; and

producing for the panel a first calibrated profile data set and a secondprofile data set, wherein (i) each member of the first calibratedprofile data set is a function of a corresponding member of the firstprofile data set and a corresponding member of a baseline profile dataset for the panel, wherein each member of the baseline data set is anormative measure, determined with respect to a relevant population ofsubjects, of the amount of one of the constituents in the panel, and(ii) each member of the second calibrated profile data set is a functionof a corresponding member of the second profile data set and acorresponding member of the baseline profile data set, the calibratedprofile data sets providing a measure of the effect by the first agenton the biological condition in relation to the effect by the secondagent.

In this embodiment, in deriving the first and second profile data sets,such measure is performed for each constituent both under conditionswherein specificity and efficiencies of amplification for allconstituents are substantially similar and under substantiallyrepeatable conditions. In a further related embodiment, the first agentis a first drug and the second agent is a second drug. In anotherrelated embodiment, the first agent is a drug and the second agent is acomplex mixture. In yet another related embodiment, the first agent is adrug and the second agent is a nutriceutical.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the invention will be more readily understoodby reference to the following detailed description, taken with referenceto the accompanying drawings, in which:

FIG. 1A shows the results of assaying 24 genes from the SourceInflammation Gene Panel (shown in Table 1) on eight separate days duringthe course of optic neuritis in a single male subject.

FIG. 1B illustrates use of an inflammation index in relation to the dataof FIG. 1A, in accordance with an embodiment of the present invention.

FIG. 2 is a graphical illustration of the same inflammation indexcalculated at 9 different, significant clinical milestones.

FIG. 3 shows the effects of single dose treatment with 800 mg ofibuprofen in a single donor as characterized by the index.

FIG. 4 shows the calculated acute inflammation index displayedgraphically for five different conditions.

FIG. 5 shows a Viral Response Index for monitoring the progress of anupper respiratory infection (URI).

FIGS. 6 and 7 compare two different populations using Gene ExpressionProfiles (with respect to the 48 loci of the Inflammation GeneExpression Panel of Table 1).

FIG. 8 compares a normal population with a rheumatoid arthritispopulation derived from a longitudinal study.

FIG. 9 compares two normal populations, one longitudinal and the othercross sectional.

FIG. 10 shows the shows gene expression values for various individualsof a normal population.

FIG. 11 shows the expression levels for each of four genes (of theInflammation Gene Expression Panel of Table 1), of a single subject,assayed monthly over a period of eight months.

FIGS. 12 and 13 similarly show in each case the expression levels foreach of 48 genes (of the Inflammation Gene Expression Panel of Table 1),of distinct single subjects (selected in each case on the basis offeeling well and not taking drugs), assayed, in the case of FIG. 12weekly over a period of four weeks, and in the case of FIG. 13 monthlyover a period of six months.

FIG. 14 shows the effect over time, on inflammatory gene expression in asingle human subject, of the administration of an anti-inflammatorysteroid, as assayed using the Inflammation Gene Expression Panel ofTable 1.

FIG. 15, in a manner analogous to FIG. 14, shows the effect over time,via whole blood samples obtained from a human subject, administered asingle dose of prednisone, on expression of 5 genes (of the InflammationGene Expression Panel of Table 1).

FIG. 16 also shows the effect over time, on inflammatory gene expressionin a single human subject suffering from rheumatoid arthritis, of theadministration of a TNF-inhibiting compound, but here the expression isshown in comparison to the cognate locus average previously determined(in connection with FIGS. 6 and 7) for the normal (i.e., undiagnosed,healthy) population.

FIG. 17A further illustrates the consistency of inflammatory geneexpression in a population.

FIG. 17B shows the normal distribution of index values obtained from anundiagnosed population.

FIG. 17C illustrates the use of the same index as FIG. 17B, where theinflammation median for a normal population has been set to zero andboth normal and diseased subjects are plotted in standard deviationunits relative to that median.

FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene ExpressionProfiles, for the same 7 loci as in FIG. 17A, two different (responderv. non-responder) 6-subject populations of rheumatoid arthritispatients.

FIG. 19 thus illustrates use of the inflammation index for assessment ofa single subject suffering from rheumatoid arthritis, who has notresponded well to traditional therapy with methotrexate.

FIG. 20 similarly illustrates use of the inflammation index forassessment of three subjects suffering from rheumatoid arthritis, whohave not responded well to traditional therapy with methotrexate.

Each of FIGS. 21-23 shows the inflammation index for an internationalgroup of subjects, suffering from rheumatoid arthritis, undergoing threeseparate treatment regimens.

FIG. 24 illustrates use of the inflammation index for assessment of asingle subject suffering from inflammatory bowel disease.

FIG. 25 shows Gene Expression Profiles with respect to 24 loci (of theInflammation Gene Expression Panel of Table 1) for whole blood treatedwith Ibuprofen in vitro in relation to other non-steroidalanti-inflammatory drugs (NSAIDs).

FIG. 26 illustrates how the effects of two competing anti-inflammatorycompounds can be compared objectively, quantitatively, precisely, andreproducibly.

FIGS. 27 through 41 illustrate the use of gene expression panels inearly identification and monitoring of infectious disease.

FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes,developed to discriminate various bacterial conditions in a hostbiological system.

FIG. 28 shows differential expression for a single locus, IFNG, to LTAderived from three distinct sources: S. pyogenes, B. subtilis, and S.aureus.

FIGS. 29 and 30 show the response after two hours of the Inflammation48A and 48B loci respectively (discussed above in connection with FIGS.6 and 7 respectively) in whole blood to administration of aGram-positive and a Gram-negative organism.

FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and aresimilar to them, with the exception that the monitoring here occurs 6hours after administration.

FIG. 33 compares the gene expression response induced by E. coli and byan organism-free E. coli filtrate.

FIG. 34 is similar to FIG. 33, but here the compared responses are tostimuli from E. coli filtrate alone and from E. coli filtrate to whichhas been added polymyxin B.

FIG. 35 illustrates the gene expression responses induced by S. aureusat 2, 6, and 24 hours after administration.

FIGS. 36 through 41 compare the gene expression induced by E. coli andS. aureus under various concentrations and times.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Definitions

The following terms shall have the meanings indicated unless the contextotherwise requires:

“Algorithm” is a set of rules for describing a biological condition. Therule set may be defined exclusively algebraically but may also includealternative or multiple decision points requiring domain-specificknowledge, expert interpretation or other clinical indicators.

An “agent” is a “composition” or a “stimulus”, as those terms aredefined herein, or a combination of a composition and a stimulus.

“Amplification” in the context of a quantitative RT-PCR assay is afunction of the number of DNA replications that are tracked to provide aquantitative determination of its concentration. “Amplification” hererefers to a degree of sensitivity and specificity of a quantitativeassay technique. Accordingly, amplification provides a measurement ofconcentrations of constituents that is evaluated under conditionswherein the efficiency of amplification and therefore the degree ofsensitivity and reproducibility for measuring all constituents issubstantially similar.

A “baseline profile data set” is a set of values associated withconstituents of a Gene Expression Panel resulting from evaluation of abiological sample (or population of samples) under a desired biologicalcondition that is used for mathematically normative purposes. Thedesired biological condition may be, for example, the condition of asubject (or population of subjects) before exposure to an agent or inthe presence of an untreated disease or in the absence of a disease.Alternatively, or in addition, the desired biological condition may behealth of a subject or a population of subjects. Alternatively, or inaddition, the desired biological condition may be that associated with apopulation of subjects selected on the basis of at least one of agegroup, gender, ethnicity, geographic location, diet, medical disorder,clinical indicator, medication, physical activity, body mass, andenvironmental exposure.

A “biological condition” of a subject is the condition of the subject ina pertinent realm that is under observation, and such realm may includeany aspect of the subject capable of being monitored for change incondition, such as health, disease including cancer; trauma; aging;infection; tissue degeneration; developmental steps; physical fitness;obesity, and mood. As can be seen, a condition in this context may bechronic or acute or simply transient. Moreover, a targeted biologicalcondition may be manifest throughout the organism or population of cellsor may be restricted to a specific organ (such as skin, heart, eye orblood), but in either case, the condition may be monitored directly by asample of the affected population of cells or indirectly by a samplederived elsewhere from the subject. The term “biological condition”includes a “physiological condition”.

“Body fluid” of a subject includes blood, urine, spinal fluid, lymph,mucosal secretions, prostatic fluid, semen, haemolymph or any other bodyfluid known in the art for a subject.

“Calibrated profile data set” is a function of a member of a firstprofile data set and a corresponding member of a baseline profile dataset for a given constituent in a panel.

A “clinical indicator” is any physiological datum used alone or inconjunction with other data in evaluating the physiological condition ofa collection of cells or of an organism. This term includes pre-clinicalindicators.

A “composition” includes a chemical compound, a nutriceutical, apharmaceutical, a homeopathic formulation, an allopathic formulation, anaturopathic formulation, a combination of compounds, a toxin, a food, afood supplement, a mineral, and a complex mixture of substances, in anyphysical state or in a combination of physical states.

To “derive” a profile data set from a sample includes determining a setof values associated with constituents of a Gene Expression Panel either(i) by direct measurement of such constituents in a biological sample or(ii) by measurement of such constituents in a second biological samplethat has been exposed to the original sample or to matter derived fromthe original sample.

“Distinct RNA or protein constituent” in a panel of constituents is adistinct expressed product of a gene, whether RNA or protein. An“expression” product of a gene includes the gene product whether RNA orprotein resulting from translation of the messenger RNA.

A “Gene Expression Panel” is an experimentally verified set ofconstituents, each constituent being a distinct expressed product of agene, whether RNA or protein, wherein constituents of the set areselected so that their measurement provides a measurement of a targetedbiological condition.

A “Gene Expression Profile” is a set of values associated withconstituents of a Gene Expression Panel resulting from evaluation of abiological sample (or population of samples).

A “Gene Expression Profile Inflammatory Index” is the value of an indexfunction that provides a mapping from an instance of a Gene ExpressionProfile into a single-valued measure of inflammatory condition.

The “health” of a subject includes mental, emotional, physical,spiritual, allopathic, naturopathic and homeopathic condition of thesubject.

“Index” is an arithmetically or mathematically derived numericalcharacteristic developed for aid in simplifying or disclosing orinforming the analysis of more complex quantitative information. Adisease or population index may be determined by the application of aspecific algorithm to a plurality of subjects or samples with a commonbiological condition.

“Inflammation” is used herein in the general medical sense of the wordand may be an acute or chronic; simple or supporative; localized ordisseminated; cellular and tissue response, initiated or sustained byany number of chemical, physical or biological agents or combination ofagents.

“Inflammatory state” is used to indicate the relative biologicalcondition of a subject resulting from inflammation, or characterizingthe degree of inflammation.

A “large number” of data sets based on a common panel of genes is anumber of data sets sufficiently large to permit a statisticallysignificant conclusion to be drawn with respect to an instance of a dataset based on the same panel.

A “normative” condition of a subject to whom a composition is to beadministered means the condition of a subject before administration,even if the subject happens to be suffering from a disease.

A “panel” of genes is a set of genes including at least twoconstituents.

A “sample” from a subject may include a single cell or multiple cells orfragments of cells or an aliquot of body fluid, taken from the subject,by means including venipuncture, excretion, ejaculation, massage,biopsy, needle aspirate, lavage sample, scraping, surgical incision orintervention or other means known in the art.

A “Signature Profile” is an experimentally verified subset of a GeneExpression Profile selected to discriminate a biological condition,agent or physiological mechanism of action.

A “Signature Panel” is a subset of a Gene Expression Panel, theconstituents of which are selected to permit discrimination of abiological condition, agent or physiological mechanism of action.

A “subject” is a cell, tissue, or organism, human or non-human, whetherin vivo, ex vivo or in vitro, under observation. When we refer toevaluating the biological condition of a subject based on a sample fromthe subject, we include using blood or other tissue sample from a humansubject to evaluate the human subject's condition; but we also include,for example, using a blood sample itself as the subject to evaluate, forexample, the effect of therapy or an agent upon the sample.

A “stimulus” includes (i) a monitored physical interaction with asubject, for example ultraviolet A or B, or light therapy for seasonalaffective disorder, or treatment of psoriasis with psoralen or treatmentof melanoma with embedded radioactive seeds, other radiation exposure,and (ii) any monitored physical, mental, emotional, or spiritualactivity or inactivity of a subject.

“Therapy” includes all interventions whether biological, chemical,physical, metaphysical, or combination of the foregoing, intended tosustain or alter the monitored biological condition of a subject.

The PCT patent application publication number WO 01/25473, publishedApr. 12, 2001, entitled “Systems and Methods for Characterizing aBiological Condition or Agent Using Calibrated Gene ExpressionProfiles,” filed for an invention by inventors herein, and which isherein incorporated by reference, discloses the use of Gene ExpressionPanels for the evaluation of (i) biological condition (including withrespect to health and disease) and (ii) the effect of one or more agentson biological condition (including with respect to health, toxicity,therapeutic treatment and drug interaction).

In particular, Gene Expression Panels may be used for measurement oftherapeutic efficacy of natural or synthetic compositions or stimulithat may be formulated individually or in combinations or mixtures for arange of targeted physiological conditions; prediction of toxicologicaleffects and dose effectiveness of a composition or mixture ofcompositions for an individual or in a population; determination of howtwo or more different agents administered in a single treatment mightinteract so as to detect any of synergistic, additive, negative, neutralor toxic activity; performing pre-clinical and clinical trials byproviding new criteria for pre-selecting subjects according toinformative profile data sets for revealing disease status; andconducting preliminary dosage studies for these patients prior toconducting phase 1 or 2 trials. These Gene Expression Panels may beemployed with respect to samples derived from subjects in order toevaluate their biological condition.

A Gene Expression Panel is selected in a manner so that quantitativemeasurement of RNA or protein constituents in the Panel constitutes ameasurement of a biological condition of a subject. In one kind ofarrangement, a calibrated profile data set is employed. Each member ofthe calibrated profile data set is a function of (i) a measure of adistinct constituent of a Gene Expression Panel and (ii) a baselinequantity.

We have found that valuable and unexpected results may be achieved whenthe quantitative measurement of constituents is performed underrepeatable conditions (within a degree of repeatability of measurementof better than twenty percent, and preferably five percent or better,and more preferably three percent or better). For the purposes of thisdescription and the following claims, we regard a degree ofrepeatability of measurement of better than twenty percent as providingmeasurement conditions that are “substantially repeatable”. Inparticular, it is desirable that, each time a measurement is obtainedcorresponding to the level of expression of a constituent in aparticular sample, substantially the same measurement should result forthe substantially the same level of expression. In this manner,expression levels for a constituent in a Gene Expression Panel may bemeaningfully compared from sample to sample. Even if the expressionlevel measurements for a particular constituent are inaccurate (forexample, say, 30% too low), the criterion of repeatability means thatall measurements for this constituent, if skewed, will nevertheless beskewed systematically, and therefore measurements of expression level ofthe constituent may be compared meaningfully. In this fashion valuableinformation may be obtained and compared concerning expression of theconstituent under varied circumstances.

In addition to the criterion of repeatability, it is desirable that asecond criterion also be satisfied, namely that quantitative measurementof constituents is performed under conditions wherein efficiencies ofamplification for all constituents are substantially similar (within oneto two percent and typically one percent or less). When both of thesecriteria are satisfied, then measurement of the expression level of oneconstituent may be meaningfully compared with measurement of theexpression level of another constituent in a given sample and fromsample to sample.

Present embodiments relate to the use of an index or algorithm resultingfrom quantitative measurement of constituents, and optionally inaddition, derived from either expert analysis or computational biology(a) in the analysis of complex data sets; (b) to control or normalizethe influence of uninformative or otherwise minor variances in geneexpression values between samples or subjects; (c) to simplify thecharacterization of a complex data set for comparison to other complexdata sets, databases or indices or algorithms derived from complex datasets; (d) to monitor a biological condition of a subject; (e) formeasurement of therapeutic efficacy of natural or synthetic compositionsor stimuli that may be formulated individually or in combinations ormixtures for a range of targeted physiological conditions; (f) forpredictions of toxicological effects and dose effectiveness of acomposition or mixture of compositions for an individual or in apopulation; (g) for determination of how two or more different agentsadministered in a single treatment might interact so as to detect any ofsynergistic, additive, negative, neutral of toxic activity (h) forperforming pre-clinical and clinical trials by providing new criteriafor pre-selecting subjects according to informative profile data setsfor revealing disease status and conducting preliminary dosage studiesfor these patients prior to conducting phase 1 or 2 trials.

Gene expression profiling and the use of index characterization for aparticular condition or agent or both may be used to reduce the cost ofphase 3 clinical trials and may be used beyond phase 3 trials; labelingfor approved drugs; selection of suitable medication in a class ofmedications for a particular patient that is directed to their uniquephysiology; diagnosing or determining a prognosis of a medical conditionor an infection which may precede onset of symptoms or alternativelydiagnosing adverse side effects associated with administration of atherapeutic agent; managing the health care of a patient; and qualitycontrol for different batches of an agent or a mixture of agents.

The Subject

The methods disclosed here may be applied to cells of humans, mammals orother organisms without the need for undue experimentation by one ofordinary skill in the art because all cells transcribe RNA and it isknown in the art how to extract RNA from all types of cells.

Selecting Constituents of a Gene Expression Panel

The general approach to selecting constituents of a Gene ExpressionPanel has been described in PCT application publication number WO01/25473. We have designed and experimentally verified a wide range ofGene Expression Panels, each panel providing a quantitative measure, ofbiological condition, that is derived from a sample of blood or othertissue. For each panel, experiments have verified that a Gene ExpressionProfile using the panel's constituents is informative of a biologicalcondition. (We show elsewhere that in being informative of biologicalcondition, the Gene Expression Profile can be used, among other things,to measure the effectiveness of therapy, as well as to provide a targetfor therapeutic intervention.) Examples of Gene Expression Panels, alongwith a brief description of each panel constituent, are provided intables attached hereto as follows:

Table 1. Inflammation Gene Expression Panel

Table 2. Diabetes Gene Expression Panel

Table 3. Prostate Gene Expression Panel

Table 4. Skin Response Gene Expression Panel

Table 5. Liver Metabolism and Disease Gene Expression Panel

Table 6. Endothelial Gene Expression Panel

Table 7. Cell Health and Apoptosis Gene Expression Panel

Table 8. Cytokine Gene Expression Panel

Table 9. TNF/IL1 Inhibition Gene Expression Panel

Table 10. Chemokine Gene Expression Panel

Table 11. Breast Cancer Gene Expression Panel

Table 12. Infectious Disease Gene Expression Panel

Other panels may be constructed and experimentally verified by one ofordinary skill in the art in accordance with the principles articulatedin the present application.

Design of Assays

We commonly run a sample through a panel in quadruplicate; that is, asample is divided into aliquots and for each aliquot we measureconcentrations of each constituent in a Gene Expression Panel. Over atotal of 900 constituent assays, with each assay conducted inquadruplicate, we found an average coefficient of variation, (standarddeviation/average)*100, of less than 2 percent, typically less than 1percent, among results for each assay. This figure is a measure of whatwe call “intra-assay variability”. We have also conducted assays ondifferent occasions using the same sample material. With 72 assays,resulting from concentration measurements of constituents in a panel of24 members, and such concentration measurements determined on threedifferent occasions over time, we found an average coefficient ofvariation of less than 5 percent, typically less than 2 percent. Weregard this as a measure of what we call “inter-assay variability”.

We have found it valuable in using the quadruplicate test results toidentify and eliminate data points that are statistical “outliers”; suchdata points are those that differ by a percentage greater, for example,than 3% of the average of all four values and that do not result fromany systematic skew that is greater, for example, than 1%. Moreover, ifmore than one data point in a set of four is excluded by this procedure,then all data for the relevant constituent is discarded.

Measurement of Gene Expression for a Constituent in the Panel

For measuring the amount of a particular RNA in a sample, we have usedmethods known to one of ordinary skill in the art to extract andquantify transcribed RNA from a sample with respect to a constituent ofa Gene Expression Panel. (See detailed protocols below. Also see PCTapplication publication number WO 98/24935 herein incorporated byreference for RNA analysis protocols). Briefly, RNA is extracted from asample such as a tissue, body fluid, or culture medium in which apopulation of a subject might be growing. For example, cells may belysed and RNA eluted in a suitable solution in which to conduct a DNAsereaction. First strand synthesis may be performed using a reversetranscriptase. Gene amplification, more specifically quantitative PCRassays, can then conducted and the gene of interest size calibratedagainst a marker such as 18S rRNA (Hirayama et al., Blood 92, 1998:46-52). Samples are measured in multiple duplicates, for example, 4replicates. Relative quantitation of the mRNA is determined by thedifference in threshhold cycles between the internal control and thegene of interest. In an embodiment of the invention, quantitative PCR isperformed using amplification, reporting agents and instruments such asthose supplied commercially by Applied Biosystems (Foster City, Calif.).Given a defined efficiency of amplification of target transcripts, thepoint (e.g., cycle number) that signal from amplified target template isdetectable may be directly related to the amount of specific messagetranscript in the measured sample. Similarly, other quantifiable signalssuch as fluorescence, enzyme activity, disintegrations per minute,absorbance, etc., when correlated to a known concentration of targettemplates (e.g., a reference standard curve) or normalized to a standardwith limited variability can be used to quantify the number of targettemplates in an unknown sample.

Although not limited to amplification methods, quantitative geneexpression techniques may utilize amplification of the targettranscript. Alternatively or in combination with amplification of thetarget transcript, amplification of the reporter signal may also beused. Amplification of the target template may be accomplished byisothermic gene amplification strategies, or by gene amplification bythermal cycling such as PCR.

It is desirable to obtain a definable and reproducible correlationbetween the amplified target or reporter and the concentration ofstarting templates. We have discovered that this objective can beachieved by careful attention to, for example, consistentprimer-template ratios and a strict adherence to a narrow permissiblelevel of experimental amplification efficiencies (for example 99.0 to100% relative efficiency, typically 99.8 to 100% relative efficiency).For example, in determining gene expression levels with regard to asingle Gene Expression Profile, it is necessary that all constituents ofthe panels maintain a similar and limited range of primer templateratios (for example, within a 10-fold range) and amplificationefficiencies (within, for example, less than 1%) to permit accurate andprecise relative measurements for each constituent. We regardamplification efficiencies as being “substantially similar”, for thepurposes of this description and the following claims, if they differ byno more than approximately 10%. Preferably they should differ by lessthan approximately 2% and more preferably by less than approximately 1%.These constraints should be observed over the entire range ofconcentration levels to be measured associated with the relevantbiological condition. While it is thus necessary for various embodimentsherein to satisfy criteria that measurements are achieved undermeasurement conditions that are substantially repeatable and whereinspecificity and efficiencies of amplification for all constituents aresubstantially similar, nevertheless, it is within the scope of thepresent invention as claimed herein to achieve such measurementconditions by adjusting assay results that do not satisfy these criteriadirectly, in such a manner as to compensate for errors, so that thecriteria are satisfied after suitable adjustment of assay results.

In practice, we run tests to assure that these conditions are satisfied.For example, we typically design and manufacture a number ofprimer-probe sets, and determine experimentally which set gives the bestperformance. Even though primer-probe design and manufacture can beenhanced using computer techniques known in the art, and notwithstandingcommon practice, we still find that experimental validation is useful.Moreover, in the course of experimental validation, we associate withthe selected primer-probe combination a set of features:

The reverse primer should be complementary to the coding DNA strand. Inone embodiment, the primer should be located across an intron-exonjunction, with not more than three bases of the three-prime end of thereverse primer complementary to the proximal exon. (If more than threebases are complementary, then it would tend to competitively amplifygenomic DNA.)

In an embodiment of the invention, the primer probe should amplify cDNAof less than 110 bases in length and should not amplify genomic DNA ortranscripts or cDNA from related but biologically irrelevant loci.

A suitable target of the selected primer probe is first strand cDNA,which may be prepared, in one embodiment, is described as follows:

(a) Use of whole blood for ex vivo assessment of a biological conditionaffected by an agent.

Human blood is obtained by venipuncture and prepared for assay byseparating samples for baseline, no stimulus, and stimulus withsufficient volume for at least three time points. Typical stimuliinclude lipopolysaccharide (LPS), phytohemagglutinin (PHA) andheat-killed staphylococci (HKS) or carrageean and may be usedindividually (typically) or in combination. The aliquots of heparinized,whole blood are mixed without stimulus and held at 37° C. in anatmosphere of 5% CO₂ for 30 minutes. Stimulus is added at varyingconcentrations, mixed and held loosely capped at 37° C. for 30 min.Additional test compounds may be added at this point and held forvarying times depending on the expected pharmacokinetics of the testcompound. At defined times, cells are collected by centrifugation, theplasma removed and RNA extracted by various standard means.

Nucleic acids, RNA and or DNA are purified from cells, tissues or fluidsof the test population or indicator cell lines. RNA is preferentiallyobtained from the nucleic acid mix using a variety of standardprocedures (or RNA Isolation Strategies, pp. 55-104, in RNAMethodologies, A laboratory guide for isolation and characterization,2nd edition, 1998, Robert E. Farrell, Jr., Ed., Academic Press), in thepresent using a filter-based RNA isolation system from Ambion(RNAqueous™, Phenol-free Total RNA Isolation Kit, Catalog #1912, version9908; Austin, Tex.).

In accordance with one procedure, the whole blood assay for GeneExpression Profiles determination was carried out as follows: Humanwhole blood was drawn into 10 mL Vacutainer tubes with Sodium Heparin.Blood samples were mixed by gently inverting tubes 4-5 times. The bloodwas used within 10-15 minutes of draw. In the experiments, blood wasdiluted 2-fold, i.e. per sample per time point, 0.6 mL whole blood+0.6mL stimulus. The assay medium was prepared and the stimulus added asappropriate.

A quantity (0.6 mL) of whole blood was then added into each 12×75 mmpolypropylene tube. 0.6 mL of 2×LPS (from E. coli serotype 0127:B8,Sigma#L3880 or serotype 055, Sigma #L4005, 10 ng/ml, subject to changein different lots) into LPS tubes was added. Next, 0.6 mL assay mediumwas added to the “control” tubes with duplicate tubes for eachcondition. The caps were closed tightly. The tubes were inverted 2-3times to mix samples. Caps were loosened to first stop and the tubesincubated@37° C., 5% CO₂ for 6 hours. At 6 hours, samples were gentlymixed to resuspend blood cells, and 1 mL was removed from each tube(using a micropipettor with barrier tip), and transfered to a 2 mL“dolphin” microfuge tube (Costar #3213).

The samples were then centrifuged for 5 min at 500×g, ambienttemperature (IEC centrifuge or equivalent, in microfuge tube adapters inswinging bucket), and as much serum from each tube was removed aspossible and discarded. Cell pellets were placed on ice; and RNAextracted as soon as possible using an Ambion RNAqueous kit.

a) Amplification Strategies.

Specific RNAs are amplified using message specific primers or randomprimers. The specific primers are synthesized from data obtained frompublic databases (e.g., Unigene, National Center for BiotechnologyInformation, National Library of Medicine, Bethesda, Md.), includinginformation from genomic and cDNA libraries obtained from humans andother animals. Primers are chosen to preferentially amplify fromspecific RNAs obtained from the test or indicator samples, see, forexample, RT PCR, Chapter 15 in RNA Methodologies, A laboratory guide forisolation and characterization, 2nd edition, 1998, Robert E. Farrell,Jr., Ed., Academic Press; or Chapter 22 pp. 143-151, RNA isolation andcharacterization protocols, Methods in molecular biology, Volume 86,1998, R. Rapley and D. L. Manning Eds., Human Press, or 14 inStatistical refinement of primer design parameters, Chapter 5, pp.55-72, PCR applications: protocols for functional genomics, M. A. Innis,D. H. Gelfand and J. J. Sninsky, Eds., 1999, Academic Press).Amplifications are carried out in either isothermic conditions or usinga thermal cycler (for example, ABI 9600 or 9700 or 7700 obtained fromApplied Biosystems, Foster City, Calif.; see Nucleic acid detectionmethods, pp. 1-24, in Molecular methods for virus detection, D. L.Wiedbrauk and D. H., Farkas, Eds., 1995, Academic Press). Amplifiednucleic acids are detected using fluorescent-tagged detection primers(see, for example, Taqman™ PCR Reagent Kit, Protocol, part number 402823revision A, 1996, Applied Biosystems, Foster City Calif.) that areidentified and synthesized from publicly known databases as describedfor the amplification primers. In the present case, amplified DNA isdetected and quantified using the ABI Prism 7700 Sequence DetectionSystem obtained from Applied Biosystems (Foster City, Calif.). Amountsof specific RNAs contained in the test sample or obtained from theindicator cell lines can be related to the relative quantity offluorescence observed (see for example, Advances in quantitative PCRtechnology: 5′ nuclease assays, Y. S. Lie and C. J. Petropolus, CurrentOpinion in Biotechnology, 1998, 9:43-48, or Rapid thermal cycling andPCR kinetics, pp. 211-229, chapter 14 in PCR applications: protocols forfunctional genomics, M. A. Innis, D. H. Gelfand and J. J. Sninsky, Eds.,1999, Academic Press).

As a particular implementation of the approach described here, wedescribe in detail a procedure for synthesis of first strand cDNA foruse in PCR. This procedure can be used for both whole blood RNA and RNAextracted from cultured cells (i.e. THP-1 cells).

Materials

1. Applied Biosystems TAQMAN Reverse Transcription Reagents Kit (P/N808-0234). Kit Components: 10× TaqMan RT Buffer, 25 mM Magnesiumchloride, deoxyNTPs mixture, Random Hexamers, RNase Inhibitor,MultiScribe Reverse Transcriptase (50 U/mL) (2) RNase/DNase free water(DEPC Treated Water from Ambion (P/N 9915G), or equivalent)

Methods

1. Place RNase Inhibitor and MultiScribe Reverse Transcriptase on iceimmediately. All other reagents can be thawed at room temperature andthen placed on ice.

2. Remove RNA samples from −80° C. freezer and thaw at room temperatureand then place immediately on ice.

3. Prepare the following cocktail of Reverse Transcriptase Reagents foreach 100 mL RT reaction (for multiple samples, prepare extra cocktail toallow for pipetting error):

1 reaction (mL) 11X, e.g. 10 samples (mL) 10× RT Buffer 10.0 110.0 25 mMMgCl2 22.0 242.0 dNTPs 20.0 220.0 Random Hexamers 5.0 55.0 RNAseInhibitor 2.0 22.0 Reverse Transcriptase 2.5 27.5 Water 18.5 203.5Total: 80.0 880.0 (80 mL per sample)

4. Bring each RNA sample to a total volume of 20 mL in a 1.5 mLmicrocentrifuge tube (for example, for THP-1 RNA, remove 10 mL RNA anddilute to 20 mL with RNase/DNase free water, for whole blood RNA use 20mL total RNA) and add 80 mL RT reaction mix from step 5,2,3. Mix bypipetting up and down.

5. Incubate sample at room temperature for 10 minutes.

6. Incubate sample at 37° C. for 1 hour.

7. Incubate sample at 90° C. for 10 minutes.

8. Quick spin samples in microcentrifuge.

9. Place sample on ice if doing PCR immediately, otherwise store sampleat −20° C. for future use.

10. PCR QC should be run on all RT samples using 18 S and b-actin (seeSOP 200-020).

The use of the primer probe with the first strand cDNA as describedabove to permit measurement of constituents of a Gene Expression Panelis as follows:

Set up of a 24-gene Human Gene Expression Panel for Inflammation.

Materials

1. 20× Primer/Probe Mix for each gene of interest.

2. 20× Primer/Probe Mix for 18S endogenous control.

3. 2× Taqman Universal PCR Master Mix.

4. cDNA transcribed from RNA extracted from cells.

5. Applied Biosystems 96-Well Optical Reaction Plates.

6. Applied Biosystems Optical Caps, or optical-clear film.

7. Applied Biosystem Prism 7700 Sequence Detector.

Methods

1. Make stocks of each Primer/Probe mix containing the Primer/Probe forthe gene of interest, Primer/Probe for 18S endogenous control, and2.times.PCR Master Mix as follows. Make sufficient excess to allow forpipetting error e.g. approximately 10% excess. The following exampleillustrates a typical set up for one gene with quadruplicate samplestesting two conditions (2 plates).

9X 1X (1 well) (2 plates worth)  2X Master Mix 12.50 112.50 20X 18SPrimer/Probe Mix 1.25 11.25 20X Gene of interest Primer/Probe Mix 1.2511.25 Total 15.00 135.00

2. Make stocks of cDNA targets by diluting 95 μl of cDNA into 2000 μl ofwater. The amount of cDNA is adjusted to give Ct values between 10 and18, typically between 12 and 13.

3. Pipette 15 μl of Primer/Probe mix into the appropriate wells of anApplied Biosystems 96-Well Optical Reaction Plate.

4. Pipette 10 μl of cDNA stock solution into each well of the AppliedBiosystems 96-Well Optical Reaction Plate.

5. Seal the plate with Applied Biosystems Optical Caps, or optical-clearfilm.

6. Analyze the plate on the AB Prism 7700 Sequence Detector.

Methods herein may also be applied using proteins where sensitivequantitative techniques, such as an Enzyme Linked ImmunoSorbent Assay(ELISA) or mass spectroscopy, are available and well-known in the artfor measuring the amount of a protein constituent. (see WO 98/24935herein incorporated by reference).

Baseline Profile Data Sets

The analyses of samples from single individuals and from large groups ofindividuals provide a library of profile data sets relating to aparticular panel or series of panels. These profile data sets may bestored as records in a library for use as baseline profile data sets. Asthe term “baseline” suggests, the stored baseline profile data setsserve as comparators for providing a calibrated profile data set that isinformative about a biological condition or agent. Baseline profile datasets may be stored in libraries and classified in a number ofcross-referential ways. One form of classification may rely on thecharacteristics of the panels from which the data sets are derived.Another form of classification may be by particular biologicalcondition. The concept of biological condition encompasses any state inwhich a cell or population of cells may be found at any one time. Thisstate may reflect geography of samples, sex of subjects or any otherdiscriminator. Some of the discriminators may overlap. The libraries mayalso be accessed for records associated with a single subject orparticular clinical trial. The classification of baseline profile datasets may further be annotated with medical information about aparticular subject, a medical condition, a particular agent etc.

The choice of a baseline profile data set for creating a calibratedprofile data set is related to the biological condition to be evaluated,monitored, or predicted, as well as, the intended use of the calibratedpanel, e.g., as to monitor drug development, quality control or otheruses. It may be desirable to access baseline profile data sets from thesame subject for whom a first profile data set is obtained or fromdifferent subject at varying times, exposures to stimuli, drugs orcomplex compounds; or may be derived from like or dissimilarpopulations.

The profile data set may arise from the same subject for which the firstdata set is obtained, where the sample is taken at a separate or similartime, a different or similar site or in a different or similarphysiological condition. For example, FIG. 5 provides a protocol inwhich the sample is taken before stimulation or after stimulation. Theprofile data set obtained from the unstimulated sample may serve as abaseline profile data set for the sample taken after stimulation. Thebaseline data set may also be derived from a library containing profiledata sets of a population of subjects having some definingcharacteristic or biological condition. The baseline profile data setmay also correspond to some ex vivo or in vitro properties associatedwith an in vitro cell culture. The resultant calibrated profile datasets may then be stored as a record in a database or library (FIG. 6)along with or separate from the baseline profile data base andoptionally the first profile data set although the first profile dataset would normally become incorporated into a baseline profile data setunder suitable classification criteria. The remarkable consistency ofGene Expression Profiles associated with a given biological conditionmakes it valuable to store profile data, which can be used, among otherthings for normative reference purposes. The normative reference canserve to indicate the degree to which a subject conforms to a givenbiological condition (healthy or diseased) and, alternatively or inaddition, to provide a target for clinical intervention.

Selected baseline profile data sets may be also be used as a standard bywhich to judge manufacturing lots in terms of efficacy, toxicity, etc.Where the effect of a therapeutic agent is being measured, the baselinedata set may correspond to Gene Expression Profiles taken beforeadministration of the agent. Where quality control for a newlymanufactured product is being determined, the baseline data set maycorrespond with a gold standard for that product. However, any suitablenormalization techniques may be employed. For example, an averagebaseline profile data set is obtained from authentic material of anaturally grown herbal nutriceutical and compared over time and overdifferent lots in order to demonstrate consistency, or lack ofconsistency, in lots of compounds prepared for release.

Calibrated Data

Given the repeatability we have achieved in measurement of geneexpression, described above in connection with “Gene Expression Panels”and “gene amplification”, we conclude that where differences occur inmeasurement under such conditions, the differences are attributable todifferences in biological condition. Thus we have found that calibratedprofile data sets are highly reproducible in samples taken from the sameindividual under the same conditions. We have similarly found thatcalibrated profile data sets are reproducible in samples that arerepeatedly tested. We have also found repeated instances whereincalibrated profile data sets obtained when samples from a subject areexposed ex vivo to a compound are comparable to calibrated profile datafrom a sample that has been exposed to a sample in vivo. We have alsofound, importantly, that an indicator cell line treated with an agentcan in many cases provide calibrated profile data sets comparable tothose obtained from in vivo or ex vivo populations of cells. Moreover,we have found that administering a sample from a subject onto indicatorcells can provide informative calibrated profile data sets with respectto the biological condition of the subject including the health, diseasestates, therapeutic interventions, aging or exposure to environmentalstimuli or toxins of the subject.

Calculation of Calibrated Profile Data Sets and Computational Aids

The calibrated profile data set may be expressed in a spreadsheet orrepresented graphically for example, in a bar chart or tabular form butmay also be expressed in a three dimensional representation. Thefunction relating the baseline and profile data may be a ratio expressedas a logarithm. The constituent may be itemized on the x-axis and thelogarithmic scale may be on the y-axis. Members of a calibrated data setmay be expressed as a positive value representing a relative enhancementof gene expression or as a negative value representing a relativereduction in gene expression with respect to the baseline.

Each member of the calibrated profile data set should be reproduciblewithin a range with respect to similar samples taken from the subjectunder similar conditions. For example, the calibrated profile data setsmay be reproducible within one order of magnitude with respect tosimilar samples taken from the subject under similar conditions. Moreparticularly, the members may be reproducible within 50%, moreparticularly reproducible within 20%, and typically within 10%. Inaccordance with embodiments of the invention, a pattern of increasing,decreasing and no change in relative gene expression from each of aplurality of gene loci examined in the Gene Expression Panel may be usedto prepare a calibrated profile set that is informative with regards toa biological condition, biological efficacy of an agent treatmentconditions or for comparison to populations. Patterns of this nature maybe used to identify likely candidates for a drug trial, used alone or incombination with other clinical indicators to be diagnostic orprognostic with respect to a biological condition or may be used toguide the development of a pharmaceutical or nutriceutical throughmanufacture, testing and marketing.

The numerical data obtained from quantitative gene expression andnumerical data from calibrated gene expression relative to a baselineprofile data set may be stored in databases or digital storage mediumsand may retrieved for purposes including managing patient health care orfor conducting clinical trials or for characterizing a drug. The datamay be transferred in physical or wireless networks via the World WideWeb, email, or internet access site for example or by hard copy so as tobe collected and pooled from distant geographic sites (FIG. 8).

In an embodiment of the present invention, a descriptive record isstored in a single database or multiple databases where the stored dataincludes the raw gene expression data (first profile data set) prior totransformation by use of a baseline profile data set, as well as arecord of the baseline profile data set used to generate the calibratedprofile data set including for example, annotations regarding whetherthe baseline profile data set is derived from a particular SignaturePanel and any other annotation that facilitates interpretation and useof the data.

Because the data is in a universal format, data handling may readily bedone with a computer. The data is organized so as to provide an outputoptionally corresponding to a graphical representation of a calibrateddata set.

For example, a distinct sample derived from a subject being at least oneof RNA or protein may be denoted as P_(I). The first profile data setderived from sample P_(I) is denoted M_(j), where M_(j) is aquantitative measure of a distinct RNA or protein constituent of P_(I).The record Ri is a ratio of M and P and may be annotated with additionaldata on the subject relating to, for example, age, diet, ethnicity,gender, geographic location, medical disorder, mental disorder,medication, physical activity, body mass and environmental exposure.Moreover, data handling may further include accessing data from a secondcondition database which may contain additional medical data notpresently held with the calibrated profile data sets. In this context,data access may be via a computer network.

The above described data storage on a computer may provide theinformation in a form that can be accessed by a user. Accordingly, theuser may load the information onto a second access site includingdownloading the information. However, access may be restricted to usershaving a password or other security device so as to protect the medicalrecords contained within. A feature of this embodiment of the inventionis the ability of a user to add new or annotated records to the data setso the records become part of the biological information.

The graphical representation of calibrated profile data sets pertainingto a product such as a drug provides an opportunity for standardizing aproduct by means of the calibrated profile, more particularly asignature profile. The profile may be used as a feature with which todemonstrate relative efficacy, differences in mechanisms of actions,etc. compared to other drugs approved for similar or different uses.

The various embodiments of the invention may be also implemented as acomputer program product for use with a computer system. The product mayinclude program code for deriving a first profile data set and forproducing calibrated profiles. Such implementation may include a seriesof computer instructions fixed either on a tangible medium, such as acomputer readable medium (for example, a diskette, CD-ROM, ROM, or fixeddisk), or transmittable to a computer system via a modem or otherinterface device, such as a communications adapter coupled to a network.The network coupling may be for example, over optical or wiredcommunications lines or via wireless techniques (for example, microwave,infrared or other transmission techniques) or some combination of these.The series of computer instructions preferably embodies all or part ofthe functionality previously described herein with respect to thesystem. Those skilled in the art should appreciate that such computerinstructions can be written in a number of programming languages for usewith many computer architectures or operating systems. Furthermore, suchinstructions may be stored in any memory device, such as semiconductor,magnetic, optical or other memory devices, and may be transmitted usingany communications technology, such as optical, infrared, microwave, orother transmission technologies. It is expected that such a computerprogram product may be distributed as a removable medium withaccompanying printed or electronic documentation (for example, shrinkwrapped software), preloaded with a computer system (for example, onsystem ROM or fixed disk), or distributed from a server or electronicbulletin board over a network (for example, the Internet or World WideWeb). In addition, a computer system is further provided includingderivative modules for deriving a first data set and a calibrationprofile data set.

The calibration profile data sets in graphical or tabular form, theassociated databases, and the calculated index or derived algorithm,together with information extracted from the panels, the databases, thedata sets or the indices or algorithms are commodities that can be soldtogether or separately for a variety of purposes as described in WO01/25473.

Index Construction

In combination, (i) the remarkable consistency of Gene ExpressionProfiles with respect to a biological condition across a population and(ii) the use of procedures that provide substantially reproduciblemeasurement of constituents in a Gene Expression Panel giving rise to aGene Expression Profile, under measurement conditions whereinspecificity and efficiencies of amplification for all constituents ofthe panel are substantially similar, make possible the use of an indexthat characterizes a Gene Expression Profile, and which thereforeprovides a measurement of a biological condition.

An index may be constructed using an index function that maps values ina Gene Expression Profile into a single value that is pertinent to thebiological condition at hand. The values in a Gene Expression Profileare the amounts of each constituent of the Gene Expression Panel thatcorresponds to the Gene Expression Profile. These constituent amountsform a profile data set, and the index function generates a singlevalue—the index—from the members of the profile data set.

The index function may conveniently be constructed as a linear sum ofterms, each term being what we call a “contribution function” of amember of the profile data set. For example, the contribution functionmay be a constant times a power of a member of the profile data set. Sothe index function would have the form

I=ΣC _(i) M _(i) ^(P(i)),

where I is the index, M_(i) is the value of the member i of the profiledata set, C_(i) is a constant, and P(i) is a power to which M_(i) israised, the sum being formed for all integral values of i up to thenumber of members in the data set. We thus have a linear polynomialexpression.

The values C_(i) and P(i) may be determined in a number of ways, so thatthe index I is informative of the pertinent biological condition. Oneway is to apply statistical techniques, such as latent class modeling,to the profile data sets to correlate clinical data or experimentallyderived data, or other data pertinent to the biological condition. Inthis connection, for example, may be employed the software fromStatistical Innovations, Belmont, Mass., called Latent Gold®. See theweb pages at www.statisticalinnovations.com/lg/, which are herebyincorporated herein by reference.

Alternatively, other simpler modeling techniques may be employed in amanner known in the art. The index function for inflammation may beconstructed, for example, in a manner that a greater degree ofinflammation (as determined by the a profile data set for theInflammation Gene Expression Profile) correlates with a large value ofthe index function. In a simple embodiment, therefore, each P(i) may be+1 or −1, depending on whether the constituent increases or decreaseswith increasing inflammation. As discussed in further detail below, wehave constructed a meaningful inflammation index that is proportional tothe expression

¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10},

where the braces around a constituent designate measurement of suchconstituent and the constituents are a subset of the Inflammation GeneExpression Panel of Table 1.

Just as a baseline profile data set, discussed above, can be used toprovide an appropriate normative reference, and can even be used tocreate a Calibrated profile data set, as discussed above, based on thenormative reference, an index that characterizes a Gene ExpressionProfile can also be provided with a normative value of the indexfunction used to create the index. This normative value can bedetermined with respect to a relevant population, so that the index maybe interpreted in relation to the normative value. The relevantpopulation may have in common a property that is at least one of agegroup, gender, ethnicity, geographic location, diet, medical disorder,clinical indicator, medication, physical activity, body mass, andenvironmental exposure.

As an example, the index can be constructed, in relation to a normativeGene Expression Profile for a population of healthy subjects, in such away that a reading of approximately 1 characterizes normative GeneExpression Profiles of healthy subjects. Let us further assume that thebiological condition that is the subject of the index is inflammation; areading of 1 in this example thus corresponds to a Gene ExpressionProfile that matches the norm for healthy subjects. A substantiallyhigher reading then may identify a subject experiencing an inflammatorycondition. The use of 1 as identifying a normative value, however, isonly one possible choice; another logical choice is to use 0 asidentifying the normative value. With this choice, deviations in theindex from zero can be indicated in standard deviation units (so thatvalues lying between −1 and +1 encompass 90% of a normally distributedreference population. Since we have found that Gene Expression Profilevalues (and accordingly constructed indices based on them) tend to benormally distributed, the 0-centered index constructed in this manner ishighly informative. It therefore facilitates use of the index indiagnosis of disease and setting objectives for treatment. The choice of0 for the normative value, and the use of standard deviation units, forexample, are illustrated in FIG. 17B, discussed below.

EXAMPLES Example 1 Acute Inflammatory Index to Assist in Analysis ofLarge, Complex Data Sets

In one embodiment of the invention the index value or algorithm can beused to reduce a complex data set to a single index value that isinformative with respect to the inflammatory state of a subject. This isillustrated in FIGS. 1A and 1B.

FIG. 1A is entitled Source Precision Inflammation Profile Tracking of ASubject Results in a Large, Complex Data Set. The figure shows theresults of assaying 24 genes from the Inflammation Gene Expression Panel(shown in Table 1) on eight separate days during the course of opticneuritis in a single male subject.

FIG. 1B shows use of an Acute Inflammation Index. The data displayed inFIG. 1A above is shown in this figure after calculation using an indexfunction proportional to the following mathematical expression:(¼{IL1A}+¼{IL1B }+¼{TNF}+¼{INFG}−1/{IL10}).

Example 2 Use of Acute Inflammation Index or Algorithm to Monitor aBiological Condition of a Sample or a Subject

The inflammatory state of a subject reveals information about the pastprogress of the biological condition, future progress, response totreatment, etc. The Acute Inflammation Index may be used to reveal suchinformation about the biological condition of a subject. This isillustrated in FIG. 2.

The results of the assay for inflammatory gene expression for each day(shown for 24 genes in each row of FIG. 1A) is displayed as anindividual histogram after calculation. The index reveals clear trendsin inflammatory status that may correlated with therapeutic intervention(FIG. 2).

FIG. 2 is a graphical illustration of the acute inflammation indexcalculated at 9 different, significant clinical milestones from bloodobtained from a single patient treated medically with for opticneuritis. Changes in the index values for the Acute Inflammation Indexcorrelate strongly with the expected effects of therapeuticintervention. Four clinical milestones have been identified on top ofthe Acute Inflammation Index in this figure including (1) prior totreatment with steroids, (2) treatment with IV solumedrol at 1 gram perday, (3) post-treatment with oral prednisone at 60 mg per day tapered to10 mg per day and (4) post treatment. The data set is the same as forFIG. 1. The index is proportional to ¼55IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10}. As expected, the acuteinflammation index falls rapidly with treatment with IV steroid, goes upduring less efficacious treatment with oral prednisone and returns tothe pre-treatment level after the steroids have been discontinued andmetabolized completely.

Example 3

Use of the acute inflammatory index to set dose including concentrationsand timing, for compounds in development or for compounds to be testedin human and non-human subjects as shown in FIG. 3. The acuteinflammation index may be used as a common reference value fortherapeutic compounds or interventions without common mechanisms ofaction. The compound that induces a gene response to a compound asindicated by the index, but fails to ameliorate a known biologicalconditions may be compared to a different compounds with varyingeffectiveness in treating the biological condition.

FIG. 3 shows the effects of single dose treatment with 800 mg ofibuprofen in a single donor as characterized by the Acute InflammationIndex. 800 mg of over-the-counter ibuprofen were taken by a singlesubject at Time=0 and Time=48 hr. Gene expression values for theindicated five inflammation-related gene loci were determined asdescribed below at times=2, 4, 6, 48, 50, 56 and 96 hours. As expectedthe acute inflammation index falls immediately after taking thenon-steroidal anti-inflammatory ibuprofen and returns to baseline after48 hours. A second dose at T=48 follows the same kinetics at the firstdose and returns to baseline at the end of the experiment at T=96.

Example 4

Use of the acute inflammation index to characterize efficacy, safety,and mode of physiological action for an agent which may be indevelopment and/or may be complex in nature. This is illustrated in FIG.4.

FIG. 4 shows that the calculated acute inflammation index displayedgraphically for five different conditions including (A) untreated wholeblood; (B) whole blood treated in vitro with DMSO, an non-active carriercompound; (C) otherwise unstimulated whole blood treated in vitro withdexamethasone (0.08 ug/ml); (D) whole blood stimulated in vitro withlipopolysaccharide, a known pro-inflammatory compound, (LPS, 1 ng/ml)and (E) whole blood treated in vitro with LPS (1 ng/ml) anddexamethasone (0.08 ug/ml). Dexamethasone is used as a prescriptioncompound that is commonly used medically as an anti-inflammatory steroidcompound. The acute inflammation index is calculated from theexperimentally determined gene expression levels of inflammation-relatedgenes expressed in human whole blood obtained from a single patient.Results of mRNA expression are expressed as Ct's in this example, butmay be expressed as, e.g., relative fluorescence units, copy number orany other quantifiable, precise and calibrated form, for the genes IL1A,IL1B, TNF, IFNG and IL10. From the gene expression values, the acuteinflammation values were determined algebraically according inproportion to the expression ¼{IL1A}+¼{IL1B}+¼{TNF}+¼{INFG}−1/{IL10}.

Example 5 Development and Use of Population Normative Values for GeneExpression Profiles

FIGS. 6 and 7 show the arithmetic mean values for gene expressionprofiles (using the 48 loci of the Inflammation Gene Expression Panel ofTable 1) obtained from whole blood of two distinct patient populations.These populations are both normal or undiagnosed. The first population,which is identified as Bonfils (the plot points for which arerepresented by diamonds), is composed of 17 subjects accepted as blooddonors at the Bonfils Blood Center in Denver, Colo. The secondpopulation is 9 donors, for which Gene Expression Profiles were obtainedfrom assays conducted four times over a four-week period. Subjects inthis second population (plot points for which are represented bysquares) were recruited from employees of Source Precision Medicine,Inc., the assignee herein. Gene expression averages for each populationwere calculated for each of 48 gene loci of the Gene ExpressionInflammation Panel. The results for loci 1-24 (sometimes referred tobelow as the Inflammation 48A loci) are shown in FIG. 6 and for loci25-48 (sometimes referred to below as the Inflammation 48B loci) areshown in FIG. 7.

The consistency between gene expression levels of the two distinctpopulations is dramatic. Both populations show gene expressions for eachof the 48 loci that are not significantly different from each other.This observation suggests that there is a “normal” expression patternfor human inflammatory genes, that a Gene Expression Profile, using theInflammation Gene Expression Panel of Table 1 (or a subset thereof)characterizes that expression pattern, and that a population-normalexpression pattern can be used, for example, to guide medicalintervention for any biological condition that results in a change fromthe normal expression pattern.

In a similar vein, FIG. 8 shows arithmetic mean values for geneexpression profiles (again using the 48 loci of the Inflammation GeneExpression Panel of Table 1) also obtained from whole blood of twodistinct patient populations. One population, expression values forwhich are represented by triangular data points, is 24 normal,undiagnosed subjects (who therefore have no known inflammatory disease).The other population, the expression values for which are represented bydiamond-shaped data points, is four patients with rheumatoid arthritisand who have failed therapy (who therefore have unstable rheumatoidarthritis).

As remarkable as the consistency of data from the two distinct normalpopulations shown in FIGS. 6 and 7 is the systematic divergence of datafrom the normal and diseased populations shown in FIG. 8. In 45 of theshown 48 inflammatory gene loci, subjects with unstable rheumatoidarthritis showed, on average, increased inflammatory gene expression(lower cycle threshold values; Ct), than subjects without disease. Thedata thus further demonstrate that is possible to identify groups withspecific biological conditions using gene expression if the precisionand calibration of the underlying assay are carefully designed andcontrolled according to the teachings herein.

FIG. 9, in a manner analogous to FIG. 8, shows the shows arithmetic meanvalues for gene expression profiles using 24 loci of the InflammationGene Expression Panel of Table 1) also obtained from whole blood of twodistinct patient populations. One population, expression values forwhich are represented by diamond-shaped data points, is 17 normal,undiagnosed subjects (who therefore have no known inflammatory disease)who are blood donors. The other population, the expression values forwhich are represented by square-shaped data points, is 16 subjects, alsonormal and undiagnosed, who have been monitored over six months, and theaverages of these expression values are represented by the square-shapeddata points. Thus the cross-sectional gene expression-value averages ofa first healthy population match closely the longitudinal geneexpression-value averages of a second healthy population, withapproximately 7% or less variation in measured expression value on agene-to-gene basis.

FIG. 10 shows the shows gene expression values (using 14 loci of theInflammation Gene Expression Panel of Table 1) obtained from whole bloodof 44 normal undiagnosed blood donors (data for 10 subjects of which isshown). Again, the gene expression values for each member of thepopulation are closely matched to those for the population, representedvisually by the consistent peak heights for each of the gene loci. Othersubjects of the population and other gene loci than those depicted heredisplay results that are consistent with those shown here.

In consequence of these principles, and in various embodiments of thepresent invention, population normative values for a Gene ExpressionProfile can be used in comparative assessment of individual subjects asto biological condition, including both for purposes of health and/ordisease. In one embodiment the normative values for a Gene ExpressionProfile may be used as a baseline in computing a “calibrated profiledata set” (as defined at the beginning of this section) for a subjectthat reveals the deviation of such subject's gene expression frompopulation normative values. Population normative values for a GeneExpression Profile can also be used as baseline values in constructingindex functions in accordance with embodiments of the present invention.As a result, for example, an index function can be constructed to revealnot only the extent of an individual's inflammation expression generallybut also in relation to normative values.

Example 6 Consistency of Expression Values, of Constituents in GeneExpression Panels, Over Time as Reliable Indicators of BiologicalCondition

FIG. 11 shows the expression levels for each of four genes (of theInflammation Gene Expression Panel of Table 1), of a single subject,assayed monthly over a period of eight months. It can be seen that theexpression levels are remarkably consistent over time.

FIGS. 12 and 13 similarly show in each case the expression levels foreach of 48 genes (of the Inflammation Gene Expression Panel of Table 1),of distinct single subjects (selected in each case on the basis offeeling well and not taking drugs), assayed, in the case of FIG. 12weekly over a period of four weeks, and in the case of FIG. 13 monthlyover a period of six months. In each case, again the expression levelsare remarkably consistent over time, and also similar acrossindividuals.

FIG. 14 also shows the effect over time, on inflammatory gene expressionin a single human subject, of the administration of an anti-inflammatorysteroid, as assayed using the Inflammation Gene Expression Panel ofTable 1. In this case, 24 of 48 loci are displayed. The subject had abaseline blood sample drawn in a PAX RNA isolation tube and then took asingle 60 mg dose of prednisone, an anti-inflammatory, prescriptionsteroid. Additional blood samples were drawn at 2 hr and 24 hr post thesingle oral dose. Results for gene expression are displayed for allthree time points, wherein values for the baseline sample are shown asunity on the x-axis. As expected, oral treatment with prednisoneresulted in the decreased expression of most of inflammation-relatedgene loci, as shown by the 2-hour post-administration bar graphs.However, the 24-hour post-administration bar graphs show that, for mostof the gene loci having reduced gene expression at 2 hours, there wereelevated gene expression levels at 24 hr.

Although the baseline in FIG. 14 is based on the gene expression valuesbefore drug intervention associated with the single individual tested,we know from the previous example, that healthy individuals tend towardpopulation normative values in a Gene Expression Profile using theInflammation Gene Expression Panel of Table 1 (or a subset of it). Weconclude from FIG. 14 that in an attempt to return the inflammatory geneexpression levels to those demonstrated in FIGS. 6 and 7 (normal or setlevels), interference with the normal expression induced a compensatorygene expression response that over-compensated for the drug-inducedresponse, perhaps because the prednisone had been significantlymetabolized to inactive forms or eliminated from the subject.

FIG. 15, in a manner analogous to FIG. 14, shows the effect over time,via whole blood samples obtained from a human subject, administered asingle dose of prednisone, on expression of 5 genes (of the InflammationGene Expression Panel of Table 1). The samples were taken at the time ofadministration (t=0) of the prednisone, then at two and 24 hours aftersuch administration. Each whole blood sample was challenged by theaddition of 0.1 ng/ml of lipopolysaccharide (a Gram-negative endotoxin)and a gene expression profile of the sample, post-challenge, wasdetermined It can seen that the two-hour sample shows dramaticallyreduced gene expression of the 5 loci of the Inflammation GeneExpression Panel, in relation to the expression levels at the time ofadministration (t=0). At 24 hours post administration, the inhibitoryeffect of the prednisone is no longer apparent, and at 3 of the 5 loci,gene expression is in fact higher than at t=0, illustratingquantitatively at the molecular level the well-known rebound effect.

FIG. 16 also shows the effect over time, on inflammatory gene expressionin a single human subject suffering from rheumatoid arthritis, of theadministration of a TNF-inhibiting compound, but here the expression isshown in comparison to the cognate locus average previously determined(in connection with FIGS. 6 and 7) for the normal (i.e., undiagnosed,healthy) population. As part of a larger international study involvingpatients with rheumatoid arthritis, the subject was followed over atwelve-week period. The subject was enrolled in the study because of afailure to respond to conservative drug therapy for rheumatoid arthritisand a plan to change therapy and begin immediate treatment with aTNF-inhibiting compound. Blood was drawn from the subject prior toinitiation of new therapy (visit 1). After initiation of new therapy,blood was drawn at 4 weeks post change in therapy (visit 2), 8 weeks(visit 3), and 12 weeks (visit 4) following the start of new therapy.Blood was collected in PAX RNA isolation tubes, held at room temperaturefor two hours and then frozen at −30° C.

Frozen samples were shipped to the central laboratory at SourcePrecision Medicine, the assignee herein, in Boulder, Colo. fordetermination of expression levels of genes in the 48-gene InflammationGene Expression Panel of Table 1. The blood samples were thawed and RNAextracted according to the manufacturer's recommended procedure. RNA wasconverted to cDNA and the level of expression of the 48 inflammatorygenes was determined. Expression results are shown for 11 of the 48 lociin FIG. 16. When the expression results for the 11 loci are comparedfrom visit one to a population average of normal blood donors from theUnited States, the subject shows considerable difference. Similarly,gene expression levels at each of the subsequent physician visits foreach locus are compared to the same normal average value. Data fromvisits 2, 3 and 4 document the effect of the change in therapy. In eachvisit following the change in the therapy, the level of inflammatorygene expression for 10 of the 11 loci is closer to the cognate locusaverage previously determined for the normal (i.e., undiagnosed,healthy) population.

FIG. 17A further illustrates the consistency of inflammatory geneexpression, illustrated here with respect to 7 loci of (of theInflammation Gene Expression Panel of Table 1), in a population of 44normal, undiagnosed blood donors. For each individual locus is shown therange of values lying within ±2 standard deviations of the meanexpression value, which corresponds to 95% of a normally distributedpopulation. Notwithstanding the great width of the confidence interval(95%), the measured gene expression value (ΔCT)—remarkably—still lieswithin 10% of the mean, regardless of the expression level involved. Asdescribed in further detail below, for a given biological condition anindex can be constructed to provide a measurement of the condition. Thisis possible as a result of the conjunction of two circumstances: (i)there is a remarkable consistency of Gene Expression Profiles withrespect to a biological condition across a population and (ii) there canbe employed procedures that provide substantially reproduciblemeasurement of constituents in a Gene Expression Panel giving rise to aGene Expression Profile, under measurement conditions whereinspecificity and efficiencies of amplification for all constituents ofthe panel are substantially similar and which therefore provides ameasurement of a biological condition. Accordingly, a function of theexpression values of representative constituent loci of FIG. 17A is hereused to generate an inflammation index value, which is normalized sothat a reading of 1 corresponds to constituent expression values ofhealthy subjects, as shown in the right-hand portion of FIG. 17A.

In FIG. 17B, an inflammation index value was determined for each memberof a population of 42 normal undiagnosed blood donors, and the resultingdistribution of index values, shown in the figure, can be seen toapproximate closely a normal distribution, notwithstanding therelatively small population size. The values of the index are shownrelative to a 0-based median, with deviations from the median calibratedin standard deviation units. Thus 90% of the population lies within +1and −1 of a 0 value. We have constructed various indices, which exhibitsimilar behavior.

FIG. 17C illustrates the use of the same index as FIG. 17B, where theinflammation median for a normal population has been set to zero andboth normal and diseased subjects are plotted in standard deviationunits relative to that median. An inflammation index value wasdetermined for each member of a normal, undiagnosed population of 70individuals (black bars). The resulting distribution of index values,shown in FIG. 17C, can be seen to approximate closely a normaldistribution. Similarly, index values were calculated for individualsfrom two diseased population groups, (1) rheumatoid arthritis patientstreated with methotrexate (MTX) who are about to change therapy to moreefficacious drugs (e.g., TNF inhibitors)(hatched bars), and (2)rheumatoid arthritis patients treated with disease modifyinganti-rheumatoid drugs (DMARDS) other than MTX, who are about to changetherapy to more efficacious drugs (e.g., MTX). Both populations presentindex values that are skewed upward (demonstrating increasedinflammation) in comparison to the normal distribution. This figure thusillustrates the utility of an index to derived from Gene ExpressionProfile data to evaluate disease status and to provide an objective andquantifiable treatment objective. When these two populations weretreated appropriately, index values from both populations returned to amore normal distribution (data not shown here).

FIG. 18 plots, in a fashion similar to that of FIG. 17A, Gene ExpressionProfiles, for the same 7 loci as in FIG. 17A, two different 6-subjectpopulations of rheumatoid arthritis patients. One population (called“stable” in the figure) is of patients who have responded well totreatment and the other population (called “unstable” in the figure) isof patients who have not responded well to treatment and whose therapyis scheduled for change. It can be seen that the expression values forthe stable population, lie within the range of the 95% confidenceinterval, whereas the expression values for the unstable population for5 of the 7 loci are outside and above this range. The right-hand portionof the figure shows an average inflammation index of 9.3 for theunstable population and an average inflammation index of 1.8 for thestable population, compared to 1 for a normal undiagnosed population.The index thus provides a measure of the extent of the underlyinginflammatory condition, in this case, rheumatoid arthritis. Hence theindex, besides providing a measure of biological condition, can be usedto measure the effectiveness of therapy as well as to provide a targetfor therapeutic intervention.

FIG. 19 thus illustrates use of the inflammation index for assessment ofa single subject suffering from rheumatoid arthritis, who has notresponded well to traditional therapy with methotrexate. Theinflammation index for this subject is shown on the far right at startof a new therapy (a TNF inhibitor), and then, moving leftward,successively, 2 weeks, 6 weeks, and 12 weeks thereafter. The index canbe seen moving towards normal, consistent with physician observation ofthe patient as responding to the new treatment.

FIG. 20 similarly illustrates use of the inflammation index forassessment of three subjects suffering from rheumatoid arthritis, whohave not responded well to traditional therapy with methotrexate, at thebeginning of new treatment (also with a TNF inhibitor), and 2 weeks and6 weeks thereafter. The index in each case can again be seen movinggenerally towards normal, consistent with physician observation of thepatients as responding to the new treatment.

Each of FIGS. 21-23 shows the inflammation index for an internationalgroup of subjects, suffering from rheumatoid arthritis, each of whom hasbeen characterized as stable (that is, not anticipated to be subjectedto a change in therapy) by the subject's treating physician. FIG. 21shows the index for each of 10 patients in the group being treated withmethotrexate, which known to alleviate symptoms without addressing theunderlying disease. FIG. 22 shows the index for each of 10 patients inthe group being treated with Enbrel (an TNF inhibitor), and FIG. 23shows the index for each 10 patients being treated with Remicade(another TNF inhibitor). It can be seen that the inflammation index foreach of the patients in FIG. 21 is elevated compared to normal, whereasin FIG. 22, the patients being treated with Enbrel as a class have aninflammation index that comes much closer to normal (80% in the normalrange). In FIG. 23, it can be seen that, while all but one of thepatients being treated with Remicade have an inflammation index at orbelow normal, two of the patients have an abnormally low inflammationindex, suggesting an immunosuppressive response to this drug. (Indeed,studies have shown that Remicade has been associated with seriousinfections in some subjects, and here the immunosuppressive effect isquantified.) Also in FIG. 23, one subject has an inflammation index thatis significantly above the normal range. This subject in fact was alsoon a regimen of an anti-inflammation steroid (prednisone) that was beingtapered; within approximately one week after the inflammation index wassampled, the subject experienced a significant flare of clinicalsymptoms.

Remarkably, these examples show a measurement, derived from the assay ofblood taken from a subject, pertinent to the subject's arthriticcondition. Given that the measurement pertains to the extent ofinflammation, it can be expected that other inflammation-basedconditions, including, for example, cardiovascular disease, may bemonitored in a similar fashion.

FIG. 24 illustrates use of the inflammation index for assessment of asingle subject suffering from inflammatory bowel disease, for whomtreatment with Remicade was initiated in three doses. The graphs showthe inflammation index just prior to first treatment, and then 24 hoursafter the first treatment; the index has returned to the normal range.The index was elevated just prior to the second dose, but in the normalrange prior to the third dose. Again, the index, besides providing ameasure of biological condition, is here used to measure theeffectiveness of therapy (Remicade), as well as to provide a target fortherapeutic intervention in terms of both dose and schedule.

FIG. 25 shows Gene Expression Profiles with respect to 24 loci (of theInflammation Gene Expression Panel of Table 1) for whole blood treatedwith Ibuprofen in vitro in relation to other non-steroidalanti-inflammatory drugs (NSAIDs). The profile for Ibuprofen is in front.It can be seen that all of the NSAIDs, including Ibuprofen share asubstantially similar profile, in that the patterns of gene expressionacross the loci are similar. Notwithstanding these similarities, eachindividual drug has its own distinctive signature.

FIG. 26 illustrates how the effects of two competing anti-inflammatorycompounds can be compared objectively, quantitatively, precisely, andreproducibly. In this example, expression of each of a panel of twogenes (of the Inflammation Gene Expression Panel of Table 1) is measuredfor varying doses (0.08-250 μg/ml) of each drug in vitro in whole blood.The market leader drug shows a complex relationship between dose andinflammatory gene response. Paradoxically, as the dose is increased,gene expression for both loci initially drops and then increases in thecase the case of the market leader. For the other compound, a moreconsistent response results, so that as the dose is increased, the geneexpression for both loci decreases more consistently.

FIGS. 27 through 41 illustrate the use of gene expression panels inearly identification and monitoring of infectious disease. These figuresplot the response, in expression products of the genes indicated, inwhole blood, to the administration of various infectious agents orproducts associated with infectious agents. In each figure, the geneexpression levels are “calibrated”, as that term is defined herein, inrelation to baseline expression levels determined with respect to thewhole blood prior to administration of the relevant infectious agent. Inthis respect the figures are similar in nature to various figures of ourbelow-referenced patent application WO 01/25473 (for example, FIG. 15therein). The concentration change is shown ratiometrically, and thebaseline level of 1 for a particular gene locus corresponds to anexpression level for such locus that is the same, monitored at therelevant time after addition of the infectious agent or other stimulus,as the expression level before addition of the stimulus. Ratiometricchanges in concentration are plotted on a logarithmic scale. Bars belowthe unity line represent decreases in concentration and bars above theunity line represent increases in concentration, the magnitude of eachbar indicating the magnitude of the ratio of the change. We have shownin WO 01/25473 and other experiments that, under appropriate conditions,Gene Expression Profiles derived in vitro by exposing whole blood to astimulus can be representative of Gene Expression Profiles derived invivo with exposure to a corresponding stimulus.

FIG. 27 uses a novel bacterial Gene Expression Panel of 24 genes,developed to discriminate various bacterial conditions in a hostbiological system. Two different stimuli are employed: lipotechoic acid(LTA), a gram positive cell wall constituent, and lipopolysaccharide(LPS), a gram negative cell wall constituent. The final concentrationimmediately after administration of the stimulus was 100 ng/mL, and theratiometric changes in expression, in relation to pre-administrationlevels, were monitored for each stimulus 2 and 6 hours afteradministration. It can be seen that differential expression can beobserved as early as two hours after administration, for example, in theIFNα2 locus, as well as others, permitting discrimination in responsebetween gram positive and gram negative bacteria.

FIG. 28 shows differential expression for a single locus, IFNG, to LTAderived from three distinct sources: S. pyogenes, B. subtilis, and S.aureus. Each stimulus was administered to achieve a concentration of 100ng/mL, and the response was monitored at 1, 2, 4, 6, and 24 hours afteradministration. The results suggest that Gene Expression Profiles can beused to distinguish among different infectious agents, here differentspecies of gram positive bacteria.

FIGS. 29 and 30 show the response of the Inflammation 48A and 48B locirespectively (discussed above in connection with FIGS. 6 and 7respectively) in whole blood to administration of a stimulus of S.aureus and of a stimulus of E. coli (in the indicated concentrations,just after administration, of 10⁷ and 10⁶ CFU/mL respectively),monitored 2 hours after administration in relation to thepre-administration baseline. The figures show that many of the locirespond to the presence of the bacterial infection within two hoursafter infection.

FIGS. 31 and 32 correspond to FIGS. 29 and 30 respectively and aresimilar to them, with the exception that the monitoring here occurs 6hours after administration. More of the loci are responsive to thepresence of infection. Various loci, such as IL2, show expression levelsthat discriminate between the two infectious agents.

FIG. 33 shows the response of the Inflammation 48A loci to theadministration of a stimulus of E. coli (again in the concentration justafter administration of 10⁶ CFU/mL) and to the administration of astimulus of an E. coli filtrate containing E. coli bacteria by productsbut lacking E. coli bacteria. The responses were monitored at 2, 6, and24 hours after administration. It can be seen, for example, that theresponses over time of loci IL1B, IL18 and CSF3 to E. coli and to E.coli filtrate are different.

FIG. 34 is similar to FIG. 33, but here the compared responses are tostimuli from E. coli filtrate alone and from E. coli filtrate to whichhas been added polymyxin B, an antibiotic known to bind tolipopolysaccharide (LPS). An examination of the response of IL1B, forexample, shows that presence of polymyxin B did not affect the responseof the locus to E. coli filtrate, thereby indicating that LPS does notappear to be a factor in the response of IL1B to E. coli filtrate.

FIG. 35 illustrates the responses of the Inflammation 48A loci over timeof whole blood to a stimulus of S. aureus (with a concentration justafter administration of 10⁷ CFU/mL) monitored at 2, 6, and 24 hoursafter administration. It can be seen that response over time can involveboth direction and magnitude of change in expression. (See for example,IL5 and IL18.)

FIGS. 36 and 37 show the responses, of the Inflammation 48A and 48B locirespectively, monitored at 6 hours to stimuli from E. coli (atconcentrations of 10⁶ and 10² CFU/mL immediately after administration)and from S. aureus (at concentrations of 10⁷ and 10² CFU/mL immediatelyafter administration). It can be seen, among other things, that invarious loci, such as B7 (FIG. 36), TACI, PLA2G7, and C1QA (FIG. 37), E.coli produces a much more pronounced response than S. aureus. The datasuggest strongly that Gene Expression Profiles can be used to identifywith high sensitivity the presence of gram negative bacteria and todiscriminate against gram positive bacteria.

FIGS. 38 and 39 show the responses, of the Inflammation 48B and 48A locirespectively, monitored 2, 6, and 24 hours after administration, tostimuli of high concentrations of S. aureus and E. coli respectively (atrespective concentrations of 10⁷ and 10⁶ CFU/mL immediately afteradministration). The responses over time at many loci involve changes inmagnitude and direction. FIG. 40 is similar to FIG. 39, but shows theresponses of the Inflammation 48B loci.

FIG. 41 similarly shows the responses of the Inflammation 48A locimonitored at 24 hours after administration to stimuli highconcentrations of S. aureus and E. coli respectively (at respectiveconcentrations of 10⁷ and 10⁶ CFU/mL immediately after administration).As in the case of FIGS. 20 and 21, responses at some loci, such as GRO1and GRO2, discriminate between type of infection.

These data support our conclusion that Gene Expression Profiles withsufficient precision and calibration as described herein (1) candetermine subpopulations of individuals with a known biologicalcondition; (2) may be used to monitor the response of patients totherapy; (3) may be used to assess the efficacy and safety of therapy;and (4) may used to guide the medical management of a patient byadjusting therapy to bring one or more relevant Gene Expression Profilescloser to a target set of values, which may be normative values or otherdesired or achievable values. We have shown that Gene ExpressionProfiles may provide meaningful information even when derived from exvivo treatment of blood or other tissue. We have also shown that GeneExpression Profiles derived from peripheral whole blood are informativeof a wide range of conditions neither directly nor typically associatedwith blood.

Furthermore, in embodiments of the present invention, Gene ExpressionProfiles can also be used for characterization and early identification(including pre-symptomatic states) of infectious disease, such assepsis. This characterization includes discriminating between infectedand uninfected individuals, bacterial and viral infections, specificsubtypes of pathogenic agents, stages of the natural history ofinfection (e.g., early or late), and prognosis. Use of the algorithmicand statistical approaches discussed above to achieve suchidentification and to discriminate in such fashion is within the scopeof various embodiments herein.

TABLE 1 Inflammation Gene Expression Panel Symbol Name ClassificationDescription IL1A Interleukin 1, cytokines-chemokines- Proinflammatory;constitutively alpha growth factors and alpha inducibly expressed invariety of cells. Generally cytosolic and released only during severeinflammatory disease IL1B Interleukin 1, cytokines-chemokines-Proinflammatory; constitutively beta growth factors and induciblyexpressed by many cell types, secreted TNFA Tumor necrosiscytokines-chemokines- Proinflammatory, TH1, mediates factor, alphagrowth factors host response to bacterial stimulus, regulates cellgrowth & differentiation IL6 Interleukin 6 cytokines-chemokines- Pro-and antiinflammatory activity, (interferon, beta growth factors TH2cytokine, regulates 2) hemotopoietic system and activation of innateresponse IL8 Interleukin 8 cytokines-chemokines- Proinflammatory, majorsecondary growth factors inflammatory mediator, cell adhesion, signaltransduction, cell- cell signaling, angiogenesis, synthesized by a widevariety of cell types IFNG Interferon gamma cytokines-chemokines- Pro-and antiinflammatory activity, growth factors TH1 cytokine, nonspecificinflammatory mediator, produced by activated T-cells IL2 Interleukin 2cytokines-chemokines- T-cell growth factor, expressed by growth factorsactivated T-cells, regulates lymphocyte activation and differentiation;inhibits apoptosis, TH1 cytokine IL12B Interleukin 12cytokines-chemokines- Proinflammatory; mediator of p40 growth factorsinnate immunity, TH1 cytokine, requires co-stimulation with IL-18 toinduce IFN-g IL15 Interleukin 15 cytokines-chemokines- Proinflammatory;mediates T-cell growth factors activation, inhibits apoptosis,synergizes with IL-2 to induce IFN-g and TNF-a IL18 Interleukin 18cytokines-chemokines- Proinflammatory, TH1, innate and growth factorsaquired immunity, promotes apoptosis, requires co-stimulation with IL-1or IL-2 to induce TH1 cytokines in T-and NK-cells IL4 Interleukin 4cytokines-chemokines- Antiinflammatory; TH2; growth factors suppressesproinflammatory cytokines, increases expression of IL-1RN, regulateslymphocyte activation IL5 Interleukin 5 cytokines-chemokines- Eosinophilstimulatory factor; growth factors stimulates late B celldifferentiation to secretion of Ig IL10 Interleukin 10cytokines-chemokines- Antiinflammatory; TH2; growth factors suppressesproduction of proinflammatory cytokines IL13 Interleukin 13cytokines-chemokines- Inhibits inflammatory cytokine growth factorsproduction IL1RN Interleukin 1 cytokines-chemokines- IL1 receptorantagonist; receptor growth factors Antiinflammatory; inhibits bindingantagonist of IL-1 to IL-1 receptor by binding to receptor withoutstimulating IL- 1-like activity IL18BP IL-18 Bindingcytokines-chemokines- Implicated in inhibition of early Protein growthfactors TH1 cytokine responses TGFB1 Transforming cytokines-chemokines-Pro- and antiinflammatory activity, growth factor, growth factorsanti-apoptotic; cell-cell signaling, beta 1 can either inhibit orstimulate cell growth IFNA2 Interferon, alpha 2 cytokines-chemokines-interferon produced by growth factors macrophages with antiviral effectsGRO1 GRO1 oncogene cytokines-chemokines- AKA SCYB1; chemotactic for(melanoma growth factors neutrophils growth stimulating activity, alpha)GRO2 GRO2 oncogene cytokines-chemokines- AKA MIP2, SCYB2; Macrophagegrowth factors inflammatory protein produced by monocytes andneutrophils TNFSF5 Tumor necrosis cytokines-chemokines- ligand for CD40;expressed on the factor (ligand) growth factors surface of T cells. Itregulates B superfamily, cell function by engaging CD40 on member 5 theB cell surface TNFSF6 Tumor necrosis cytokines-chemokines- AKA FasL;Ligand for FAS factor (ligand) growth factors antigen; transducesapoptotic superfamily, 6 signals into cells CSF3 Colonycytokines-chemokines- AKA GCSF; cytokine that stimulating factor growthfactors stimulates granulocyte 3 (granulocyte) development B7 B7 proteincell signaling and Regulatory protein that may be activation associatedwith lupus CSF2 Granulocyte cytokines-chemokines- AKA GM-CSF;Hematopoietic monocyte colony growth factors growth factor; stimulatesgrowth stimulating factor and differentiation of hematopoietic precursorcells from various lineages, including granulocytes, macrophages,eosinophils, and erythrocytes TNFSF13B Tumor necrosiscytokines-chemokines- B cell activating factor, TNF factor (ligand)growth factors family superfamily, member 13b TACI Transmembranecytokines-chemokines- T cell activating factor and calcium activator andgrowth factors cyclophilin modulator CAML interactor VEGF vascularcytokines-chemokines- Producted by monocytes endothelial growth factorsgrowth factor ICAM1 Intercellular Cell Adhesion/Matrix Endothelial cellsurface molecule; adhesion Protein regulates cell adhesion and molecule1 trafficking, upregulated during cytokine stimulation PTGS2Prostaglandin- Enzyme/Redox AKA COX2; Proinflammatory, endoperoxidemember of arachidonic acid to synthase 2 prostanoid conversion pathway;induced by proinflammatory cytokines NOS2A Nitric oxide Enzyme/Redox AKAiNOS; produces NO which is synthase 2A bacteriocidal/tumoricidal PLA2G7Phospholipase Enzyme/Redox Platelet activating factor A2, group VII(platelet activating factor acetylhydrolase, plasma) HMOX1 Hemeoxygenase Enzyme/Redox Endotoxin inducible (decycling) 1 F3 F3Enzyme/Redox AKA thromboplastin, Coagulation Factor 3; cell surfaceglycoprotein responsible for coagulation catalysis CD3Z CD3 antigen,zeta Cell Marker T-cell surface glycoprotein polypeptide PTPRC proteintyrosine Cell Marker AKA CD45; mediates T-cell phosphatase, activationreceptor type, C CD14 CD14 antigen Cell Marker LPS receptor used asmarker for monocytes CD4 CD4 antigen Cell Marker Helper T-cell marker(p55) CD8A CD8 antigen, Cell Marker Suppressor T cell marker alphapolypeptide CD19 CD19 antigen Cell Marker AKA Leu 12; B cell growthfactor HSPA1A Heat shock cell signaling and heat shock protein 70 kDaprotein 70 activation MMP3 Matrix Proteinase/Proteinase AKA stromelysin;degrades metalloproteinase 3 Inhibitor fibronectin, laminin and gelatinMMP9 Matrix Proteinase/Proteinase AKA gelatinase B; degradesmetalloproteinase 9 Inhibitor extracellular matrix molecules, secretedby IL-8-stimulated neutrophils PLAU Plasminogen Proteinase/ProteinaseAKA uPA; cleaves plasminogen to activator, Inhibitor plasmin (a proteaseresponsible for urokinase nonspecific extracellular matrix degradation)SERPINE1 Serine (or Proteinase/Proteinase Plasminogen activatorinhibitor- cysteine) protease Inhibitor 1/PAI-1 inhibitor, clade B(ovalbumin), member 1 TIMP1 tissue inhibitor of Proteinase/ProteinaseIrreversibly binds and inhibits metalloproteinase 1 Inhibitormetalloproteinases, such as collagenase C1QA ComplementProteinase/Proteinase Serum complement system; forms component 1, qInhibitor C1 complex with the proenzymes subcomponent, c1r and cls alphapolypeptide HLA- Major Histocompatibility Binds antigen for presentationto DRB1 histocompatibility CD4+ cells complex, class II, DR beta 1

TABLE 2 Diabetes Gene Expression Panel Symbol Name ClassificationDescription G6PC glucose-6- Glucose-6- Catalyzes the final step in thephosphatase, phosphatase/Glycogen gluconeogenic and glycogenolyticcatalytic metabolism pathways. Stimulated by glucocorticoids andstrongly inhibited by insulin. Overexpression (in conjunction with PCK1overexpression) leads to increased hepatic glucose production. GCGglucagon pancreatic/peptide Pancreatic hormone which hormone counteractsthe glucose-lowering action of insulin by stimulating glycogenolysis andgluconeogenesis. Underexpression of glucagon is preferred. Glucagon-likepeptide (GLP-1) proposed for type 2 diabetes treatment inhibits glucagGCGR glucagon receptor glucagon receptor Expression of GCGR is stronglyupregulated by glucose. Deficiency or imbalance could play a role inNIDDM. Has been looked as a potential for gene therapy. GFPT1 glutamine-Glutamine The rate limiting enzyme for fructose-6- amidotransferaseglucose entry into the hexosamine phosphate biosynthetic pathway (HBP).transaminase 1 Overexpression of GFA in muscle and adipose tissueincreases products of the HBP which are thought to cause insulinresistance (possibly through defects to glucose) GYS1 glycogen synthaseTransferase/Glycogen A key enzyme in the regulation of 1 (muscle)metabolism glycogen synthesis in the skeletal muscles of humans.Typically stimulated by insulin, but in NIDDM individuals GS is shown tobe completely resistant to insulin stimulation (decreased activity andactivation in muscle) HK2 hexokinase 2 hexokinase Phosphorylates glucoseinto glucose-6-phosphate. NIDDM patients have lower HK2 activity whichmay contribute to insulin resistance. Similar action to GCK. INS insulinInsulin receptor ligand Decreases blood glucose concentration andaccelerates glycogen synthesis in the liver. Not as critical in NIDDM asin IDDM. IRS1 insulin receptor signal Positive regulation of insulinsubstrate 1 transduction/transmembrane action. This protein is activatedreceptor protein when insulin binds to insulin receptor - binds 85-kDasubunit of PI 3-K. decreased in skeletal muscle of obese humans. PCK1Phosphoenolpyruvate rate-limiting Rate limiting enzyme for carboxykinase1 gluconeogenic enzyme gluconeogenesis - plays a key role in theregulation of hepatic glucose output by insulin and glucagon.Overexpression in the liver results in increased hepatic glucoseproduction and hepatic insulin resistance to glycogen synthe PIK3R1phosphoinositide- regulatory enzyme Positive regulation of insulin3-kinase, action. Docks in IRS proteins and regulatory subunit, Gab1 -activity is required for polypeptide 1 (p85 insulin stimulatedtranslocation of alpha) glucose transporters to the plasma membrane andactivation of glucose uptake. PPARG peroxisome transcription The primarypharmacological proliferator- factor/Ligand- target for the treatment ofinsulin activated receptor, dependent nuclear resistance in NIDDM.Involved in gamma receptor glucose and lipid metabolism in skeletalmuscle. PRKCB1 protein kinase C, protein kinase C/protein Negativeregulation of insulin beta 1 phosphorylation action. Activated byhyperglycemia - increases phosphorylation of IRS-1 and reduces insulinreceptor kinase activity. Increased PKC activation may lead to oxidativestress causing overexpression of TGF- beta and fibronectin SLC2A2 solutecarrier glucose transporter Glucose transporters expressed family 2uniquely in b-cells and liver. (facilitated glucose Transport glucoseinto the b-cell. transporter), Typically underexpressed in member 2pancreatic islet cells of individuals with NIDDM. SLC2A4 solute carrierglucose transporter Glucose transporter protein that is family 2 finalmediator in insulin-stimulated (facilitated glucose glucose uptake (ratelimiting for transporter), glucose uptake). Underexpression member 4 notimportant, but overexpression in muscle and adipose tissue consistentlyshown to increase glucose transport. TGFB1 transforming Transforminggrowth Regulated by glucose - in NIDDM growth factor, beta 1 factor betareceptor individuals, overexpression (due to ligand oxidative stress—seePKC) promotes renal cell hypertrophy leading to diabetic nephropathy.TNF tumor necrosis cytokine/tumor necrosis Negative regulation ofinsulin factor factor receptor ligand action. Produced in excess byadipose tissue of obese individuals - increases IRS-1 phosphorylationand decreases insulin receptor kinase activity.

TABLE 3 Prostate Gene Expression Panel Symbol Name ClassificationDescription ABCC1 ATP-binding membrane transporter AKA MRP1, ABC29:cassette, sub- Multispecific organic anion family C, membranetransporter; member 1 overexpression confers tissue protection against awide variety of xenobiotics due to their removal from the cell. ACPPAcid phosphatase AKA PAP: Major phosphatase of phosphatase, theprostate; synthesized under prostate androgen regulation; secreted bythe epithelial cells of the prostrate BCL2 B-cell CLL/ apoptosisInhibitor-cell Blocks apoptosis by interfering lymphoma 2 cycle control-with the activation of caspases oncogenesis BIRC5 Baculoviral IAPapoptosis Inhibitor AKA Survivin; API4: May repeat-containing 5counteract a default induction of apoptosis in G2/M phase of cell cycle;associates with microtubules of the mitotic spindle during apoptosisCDH1 Cadherin 1, type cell-cell adhesion/ AKA ECAD, UVO: Calcium ion- 1,E-cadherin interaction dependent cell adhesion molecule that mediatescell to cell interactions in epithelial cells CDH2 Cadherin 2, typecell-cell adhesion/ AKA NCAD, CDHN: Calcium- 1, N-cadherin interactiondependent glycoprotein that mediates cell-cell interactions; may beinvolved in neuronal recognition mechanism CDKN2A Cyclin-dependent cellcycle control- AKA p16, MTS1, INK4: Tumor kinase inhibitor tumorsuppressor suppressor gene involved in a 2A variety of malignancies;arrests normal diploid cells in late G1 CTNNA1 Catenin, alpha 1 celladhesion Binds cadherins and links them with the actin cytoskeletonFOLH1 Folate Hydrolase hydrolase AKA PSMA, GCP2: Expressed in normal andneoplastic prostate cells; membrane bound glycoprotein; hydrolyzesfolate and is an N-acetylated a-linked acidic dipeptidase GSTT1Glutathione-S- Catalyzes the conjugation of Transferase, theta 1metabolism reduced glutathione to a wide number of exogenous andendogenous hydrophobic electrophiles; has an important role in humancarcinogenesis HMGIY High mobility DNA binding- Potential oncogene withMYC group protein, transcriptional binding site at promoter region;isoforms I and Y regulation-oncogene involved in the transcriptionregulation of genes containing or in close proximity to a + t-richregions HSPA1A Heat shock 70 kD cell signalling and AKA HSP-70, HSP70-1:protein 1A activation Molecular chaperone, stabilizes AU rich mRNA IGF1RInsulin-like cytokines-chemokines- Mediates insulin stimulated DNAgrowth factor 1 growth factors synthesis; mediates IGF1 receptorstimulated cell proliferation and differentiation IL6 Interleukin 6cytokines-chemokines- Pro- and anti-inflammatory growth factorsactivity, TH2 cytokine, regulates hematopoiesis, activation of innateresponse, osteoclast development; elevated in sera of patients withmetastatic cancer IL8 Interleukin 8 cytokines-chemokines- AKA SCYB8,MDNCF: growth factors Proinflammatory chemokine; major secondaryinflammatory mediator resulting in cell adhesion, signal transduction,cell-cell signaling; regulates angiogenesis in prostate cancer KAI1Kangai 1 tumor suppressor AKA SAR2, CD82, ST6: suppressor of metastaticability of prostate cancer cells KLK2 Kallikrein 2, protease-kallikreinAKA hGK-1: Glandular kallikrein; prostatic expression restricted mainlyto the prostate. KLK3 Kallikrein 3 protease-kallikrein AKA PSA:Kallikrein-like protease which functions normally in liquefaction ofseminal fluid. Elevated in prostate cancer. KRT19 Keratin 19 structuralprotein- AKA K19: Type I epidermal differentiation keratin; may formintermediate filaments KRT5 Keratin 5 structural protein- AKA EBS2: 58kD Type II keratin differentiation co-expressed with keratin 14, a 50 kDType I keratin, in stratified epithelium. KRT5 expression is a hallmarkof mitotically active keratinocytes and is the primary structuralcomponent of the 10 nm intermediate filaments of the mitotic epidermalbasal cells. KRT8 Keratin 8 structural protein- AKA K8, CK8: Type IIkeratin; differentiation coexpressed with Keratin 18; involved inintermediate filament formation LGALS8 Lectin, cell adhesion-growth AKAPCTA-1: binds to beta Galactoside- and differentiation galactoside;involved in biological binding soluble 8 processes such as celladhesion, cell growth regulation, inflammation, immunomodulation,apoptosis and metastasis MYC V-myc avian transcription factor-Transcription factor that promotes myelocytomatosis oncogene cellproliferation and viral oncogene transformation by activating homologgrowth-promoting genes; may also repress gene expression NRP1 Neuropilin1 cell adhesion AKA NRP, VEGF165R: A novel VEGF receptor that modulatesVEGF binding to KDR (VEGF receptor) and subsequent bioactivity andtherefore may regulate VEGF-induced angiogenesis; calcium-independentcell adhesion molecule that function during the formation of certainneuronal circuits PART1 Prostate Exhibits increased expression inandrogen- LNCaP cells upon exposure to regulated androgens transcript 1PCA3 Prostate cancer AKA DD3: prostate specific; antigen 3 highlyexpressed in prostate tumors PCANAP7 Prostate cancer AKA IPCA7: unknownfunction; associated protein 7 co-expressed with known prostate cancergenes PDEF Prostate transcription factor Acts as an androgen-independentepithelium transcriptional activator of the PSA specific Ets promoter;directly interacts with transcription the DNA binding domain of factorandrogen receptor and enhances androgen-mediated activation of the PSApromoter PLAU Urokinase-type proteinase AKA UPA URK: cleaves plasminogenplasminogen to plasmin activator POV1 Prostate cancer RNA expressedselectively in overexpressed prostate tumor samples gene 1 PSCA Prostatestem cell antigen Prostate-specific cell surface antigen antigenexpressed strongly by both androgen-dependent and - independent tumorsPTGS2 Prostaglandin- cytokines-chemokines- AKA COX-2: Proinflammatory;endoperoxide growth factors member of arachidonic acid to synthase 2prostanoid conversion pathway SERPINB5 Serine proteinase proteinaseinhibitor- AKA Maspin, PI5: Protease inhibitor, clade B, tumorsuppressor Inhibitor; Tumor suppressor, member 5 especially formetastasis. SERPINE1 Serine (or proteinase inhibitor AKA PAI1: regulatesfibrinolysis; cystein) inhibits PLAU proteinase inhibitor, clade E,member 1 STAT3 Signal transcription factor AKA APRF: Transcriptionfactor transduction and for acute phase response genes; activator ofrapidly activated in response to transcription 3 certain cytokines andgrowth factors; binds to IL6 response elements TERT Telomerase AKA TCS1,EST2: reverse Ribonucleoprotein which in vitro transcriptase recognizesa single-stranded G- rich telomere primer and adds multiple telomericrepeats to its 3- prime end by using an RNA template TGFB1 Transformingcytokines-chemokines- AKA DPD1, CED: Pro- and growth factor, growthfactors antiinflammatory activity; anti- beta 1 apoptotic; cell-cellsignaling, can either inhibit or stimulate cell growth TNF Tumornecrosis cytokines-chemokines- AKA TNF alpha: Proinflammatory factor,member 2 growth factors cytokine that is the primary mediator of immuneresponse and regulation, associated with TH1 responses, mediates hostresponse to bacterial stimuli, regulates cell growth & differentiationTP53 Tumor protein 53 DNA binding protein- AKA P53: Activates expressionof cell cycle-tumor genes that inhibit tumor growth suppressor and/orinvasion; involved in cell cycle regulation (required for growth arrestat G1); inhibits cell growth through activation of cell- cycle arrestand apoptosis VEGF Vascular cytokines-chemokines- AKA VPF: Inducesvascular Endothelial growth factors permeability, endothelial cellGrowth Factor proliferation, angiogenesis

TABLE 4 Skin Response Gene Expression Panel Symbol Name ClassificationDescription BAX BCL2 associated apoptosis induction Acceleratesprogrammed cell death X protein germ cell development by binding to andantagonizing the apoptosis repressor BCL2; may induce caspase activationBCL2 B-cell apoptosis inhibitor- Integral mitochondrial membraneCLL-lymphoma 2 cycle control- protein that blocks the apoptoticoncogenesis death of some cells such as lymphocytes; constitutiveexpression of BCL2 thought to be cause of follicular lymphoma BSGBasignin signal transduction- Member of Ig superfamily; tumor peripheralplasma cell-derived collagenase membrane protein stimulatory factor;stimulates matrix metalloproteinase synthesis in fibroblasts COL7A1 TypeVII collagen-differentiation- alpha 1 subunit of type VII collagen,alpha 1 extracellular matrix collagen; may link collagen fibrils to thebasement membrane CRABP2 Cellular Retinoic retinoid binding-signal Lowmolecular weight protein Acid Binding transduction- highly expressed inskin; thought Protein transcription regulation to be important inRA-mediated regulation of skin growth & differentiation CTGF Connectiveinsulin-like growth Member of family of peptides Tissue Growthfactor-differentiation- including serum-induced Factor wounding responseimmediate early gene products expressed after induction by growthfactors; overexpressed in fibrotic disorders DUSP1 Dual Specificityoxidative stress Induced in human skin fibroblasts Phosphataseresponse-tyrosine by oxidative/heat stress & growth phosphatase factors;de-phosphorylates MAP kinase erk2; may play a role in negativeregulation of cellular proliferation FGF7 Fibroblast growth growthfactor- aka KGF; Potent mitogen for factor 7 differentiation- epithelialcells; induced after skin wounding response- injury signal transductionFN1 Fibronectin cell adhesion-motility- Major cell surface glycoproteinof signal transduction many fibroblast cells; thought to have a role incell adhesion, morphology, wound healing & cell motility FOS v-fos FBJmurine transcription factor- Proto-oncoprotein acting with osteosarcomainflammatory response- JUN, stimulates transcription of virus oncogenecell growth & genes with AP-1 regulatory sites; homolog maintanence insome cases FOS expression is associated with apototic cell death GADD45AGrowth Arrest cell cycle-DNA repair- Transcriptionally induced and DNA-apoptosis following stressful growth arrest damage- conditions &treatment with DNA inducible alpha damaging agents; binds to PCNAaffecting it's interaction with some cell division protein kinase GRO1GRO1 oncogene cytokines-chemokines- AKA SCYB1; chemotactic for (melanomagrowth factors neutrophils growth stimulating activity, alpha) HMOX1Heme Oxygenase 1 metabolism- Essential enzyme in heme endoplasmicreticulum catabolism; HMOX1 induced by its substrate heme & othersubstances such as oxidizing agents & UVA ICAM1 Intercellular CellAdhesion/Matrix Endothelial cell surface molecule; adhesion Proteinregulates cell adhesion and molecule 1 trafficking, upregulated duringcytokine stimulation IL1A Interleukin 1, cytokines-chemokines-Proinflammatory; constitutively alpha growth factors and induciblyexpressed in variety of cells. Generally cytosolic and released onlyduring severe inflammatory disease IL1B Interleukin 1,cytokines-chemokines- Proinflammatory; constitutively beta growthfactors and inducibly expressed by many cell types, secreted IL8Interleukin 8 cytokines-chemokines- Proinflammatory, major secondarygrowth factors inflammatory mediator, cell adhesion, signaltransduction, cell- cell signaling, angiogenesis, synthesized by a widevariety of cell types IVL Ivolucrin structural protein- Component of thekeratinocyte peripheral plasma crosslinked envelope; first appearsmembrane protein in the cytosol becoming crosslinked to membraneproteins by transglutaminase JUN v-jun avian transcription factor-Proto-oncoprotein; component of sarcoma virus 17 DNA bindingtranscription factor AP-1 that oncogene interacts directly with targetDNA homolog sequences to regulate gene expression KRT14 Keratin 14structural protein- Type I keratin; associates with differentiation-cellshape keratin 5; component of intermediate filaments; several autosomaldominant blistering skin disorders caused by gene defects KRT16 Keratin16 structural protein- Type I keratin; component of differentiation-cellshape intermediate filaments; induced in skin conditions favoringenhanced proliferation or abnormal differentiation KRT5 Keratin 5structural protein- Type II intermediate filament chaindifferentiation-cell shape expessed largely in stratified epithelium;hallmark of mitotically active keratinocytes MAPK8 Mitogen kinase-stressresponse- aka JNK1; mitogen activated Activated Protein signaltransduction protein kinase regulates c-Jun in Kinase 8 response to cellstress; UV irradiation of skin activates MAPK8 MMP1 MatrixProteinase/Proteinase aka Collagenase; cleaves collagensMetalloproteinase 1 Inhibitor types I-III; plays a key role inremodeling occuring in both normal & diseased conditions;transcriptionally regulated by growth factors, hormones, cytokines &cellular transformation MMP2 Matrix Proteinase/Proteinase akaGelatinase; cleaves collagens Metalloproteinase 2 Inhibitor types IV, V,VII and gelatin type I; produced by normal skin fibroblasts; may play arole in regulation of vascularization & the inflammatory response MMP3Matrix Proteinase/Proteinase aka Stromelysin; degrades Metalloproteinase3 Inhibitor fibronectin, laminin, collagens III, IV, IX, X, cartilageproteoglycans, thought to be involved in wound repair; progression ofatherosclerosis & tumor initiation; produced predominantly by connectivetissue cells MMP9 Matrix Proteinase/Proteinase AKA gelatinase B;degrades metalloproteinase 9 Inhibitor extracellular matrix molecules,secreted by IL-8-stimulated neutrophils NR1I2 Nuclear receptorTranscription activation aka PAR2; Member of nuclear subfamily 1factor-signal hormone receptor family of ligand- transduction-xenobioticactivated transcription factors; metabolism activates transcription ofcytochrome P-450 genes PCNA Proliferating Cell DNA binding-DNA Requiredfor both DNA replication Nuclear Antigen replication-DNA repair- &repair; processivity factor for cell proliferation DNA polymerases deltaand epsilon PI3 Proteinase proteinase inhibitor- aka SKALP; Proteinaseinhibitor inhibitor 3 skin protein binding- found in epidermis ofseveral derived extracellular matrix inflammatory skin diseases; it'sexpression can be used as a marker of skin irritancy PLAU PlasminogenProteinase/Proteinase AKA uPA; cleaves plasminogen to activator,Inhibitor plasmin (a protease responsible for urokinase nonspecificextracellular matrix degradation) PTGS2 Prostaglandin- Enzyme/Redox akaCOX2; Proinflammatory, endoperoxide member of arachidonic acid tosynthase 2 prostanoid conversion pathway; induced by proinflammatorycytokines S100A7 S100 calcium- calcium binding- Member of S100 family ofcalcium binding protein 7 epidermal differentiation binding proteins;localized in the cytoplasm &/or nucleus of a wide range of cells;involved in the regulation of cell cycle progression & differentiation;markedly overexpressed in skin lesions of psoriatic patients TGFB1Transforming cytokines-chemokines- Pro- and antiinflammatory activity,growth factor, growth factors anti-apoptotic; cell-cell signaling, betacan either inhibit or stimulate cell growth TIMP1 Tissue Inhibitormetalloproteinases Member of TIMP family; natural of Matrixinhibitor-ECM inhibitors of matrix Metalloproteinase 1maintenance-positive metalloproteinases; control cell proliferationtranscriptionally induced by cytokines & hormones; mediateserythropoeisis in vitro TNF Tumor necrosis cytokines-chemokines-Proinflammatory, TH1, mediates factor, alpha growth factors hostresponse to bacterial stimulus, regulates cell growth & differentiationTNFSF6 Tumor necrosis ligand-apoptosis aka FASL; Apoptosis antigenfactor (ligand) induction-signal ligand 1 is the ligand for FAS;superfamily, transduction interaction of FAS with its ligand member 6 iscritical in triggering apoptosis of some types of cells such aslymphocytes; defects in protein may be related to some cases of SLE TP53tumor protein p53 transcription factor- Tumor protein p53, a nuclear DNAbinding-tumor protein, plays a role in regulation suppressor-DNA of cellcycle; binds to DNA p53 recombination/repair binding site and activatesexpression of downstream genes that inhibit growth and/or invasion oftumor VEGF vascular cytokines-chemokines- Producted by monocytesendothelial growth factor growth factor

TABLE 5 Liver Metabolism and Disease Gene Expression Panel Symbol NameClassification Description ABCC1 ATP-binding Liver Health Indicator AKAMultidrug resistance protein cassette, sub- 1; AKA CFTR/MRP;multispecific family C, member 1 organic anion membrane transporter;mediates drug resistance by pumping xenobiotics out of cell AHR Arylhydrocarbon Metabolism Increases expression of xenobiotic receptorReceptor/Transcription metabolizing enzymes (ie P450) in Factor responseto binding of planar aromatic hydrocarbons ALB Albumin Liver HealthIndicator Carrier protein found in blood serum, synthesized in theliver, downregulation linked to decreased liver function/health COL1A1Collagen, type 1, Tissue Remodelling AKA Procollagen; extracellularalpha 1 matrix protein; implicated in fibrotic processes of damagedliver CYP1A1 Cytochrome P450 Metabolism Enzyme Polycyclic aromatichydrocarbon 1A1 metabolism; monooxygenase CYP1A2 Cytochrome P450Metabolism Enzyme Polycyclic aromatic hydrocarbon 1A2 metabolism;monooxygenase CYP2C19 Cytochrome P450 Metabolism Enzyme Xenobioticmetabolism; 2C19 monooxygenase CYP2D6 Cytochrome P450 Metabolism EnzymeXenobiotic metabolism; 2D6 monooxygenase CYP2E Cytochrome P450Metabolism Enzyme Xenobiotic metabolism; 2E1 monooxygenase; catalyzesformation of reactive intermediates from small organic molecules (i.e.ethanol, acetaminophen, carbon tetrachloride) CYP3A4 Cytochrome P450Metabolism Enzyme Xenobiotic metabolism; broad 3A4 catalyticspecificity, most abundantly expressed liver P450 EPHX1 Epoxidehydrolase Metabolism Enzyme Catalyzes hydrolysis of reactive 1,microsomal epoxides to water soluble (xenobiotic) dihydrodiols FAPFibroblast Liver Health Indicator Expressed in cancer stroma andactivation protein, wound healing GST Glutathione S- Metabolism EnzymeCatalyzes glutathione conjugation transferase to metabolic substrates toform more water-soluble, excretable compounds; primer-probe setnonspecific for all members of GST family GSTA1 Glutathione S-Metabolism Enzyme Catalyzes glutathione conjugation and A2 transferase1A1/2 to metabolic substrates to form more water-soluble, excretablecompounds GSTM1 Glutathione S- Metabolism Enzyme Catalyzes glutathioneconjugation transferase M1 to metabolic substrates to form morewater-soluble, excretable compounds KITLG KIT ligand Growth Factor AKAStem cell factor (SCF); mast cell growth factor, implicated infibrosis/cirrhosis due to chronic liver inflammation LGALS3 Lectin,Liver Health Indicator AKA galectin 3; Cell growth galactoside-regulation binding, soluble, 3 NR1I2 Nuclear receptor Metabolism AKAPregnane X receptor (PXR); subfamily 1, Receptor/Transcriptionheterodimer with retinoid X group I, family 2 Factor receptor formsnuclear transcription factor for CYP3A4 NR1I3 Nuclear receptorMetabolism AKA Constitutive androstane subfamily 1,Receptor/Transcription receptor beta (CAR); heterodimer group I, family3 Factor with retinoid X receptor forms nuclear transcription factor;mediates P450 induction by phenobarbital-like inducers. ORM1 Orosomucoid1 Liver Health Indicator AKA alpha 1 acid glycoprotein (AGP), acutephase inflammation protein PPARA Peroxisome Metabolism Receptor Bindsperoxisomal proliferators (ie proliferator fatty acids, hypolipidemicdrugs) activated receptor α & controls pathway for beta- oxidation offatty acids SCYA2 Small inducible Cytokine/Chemokine AKA Monocytechemotactic cytokine A2 protein 1 (MCP1); recruits monocytes to areas ofinjury and infection, upregulated in liver inflammation UCP2 UncouplingLiver Health Indicator Decouples oxidative protein 2 phosphorylationfrom ATP synthesis, linked to diabetes, obesity UGT UDP- MetabolismEnzyme Catalyzes glucuronide conjugation Glucuronosyltransferase tometabolic substrates, primer- probe set nonspecific for all members ofUGT1 family

TABLE 6 Endothelial Gene Expression Panel Symbol Name ClassificationDescription ADAMTS1 Disintegrin-like Protease AKA METH1; Inhibitsendothelial and cell proliferation; may inhibit metalloproteaseangiogenesis; expression may be (reprolysin type) associated withdevelopment of with cancer cachexia. thrombospondin type 1 motif, 1CLDN14 Claudin 14 AKA DFNB29; Component of tight junction strands ECE1Endothelin Metalloprotease Cleaves big endothelin 1 to convertingendothelin 1 enzyme 1 EDN1 Endothelin 1 Peptide hormone AKA ET1;Endothelium-derived peptides; potent vasoconstrictor EGR1 Early growthTranscription factor AKA NGF1A; Regulates the response 1 transcriptionof genes involved in mitogenesis and differentiation FLT1 Fms-relatedAKA VEGFR1; FRT; Receptor for tyrosine kinase 1 VEGF; involved invascular (vascular development and regulation of endothelial vascularpermeability growth factor/vascular permeability factor receptor) GJA1gap junction AKA CX43; Protein component of protein, alpha 1, gapjunctions; major component of 43 kD gap junctions in the heart; may beimportant in synchronizing heart contractions and in embryonicdevelopment GSR Glutathione Oxidoreductase AKA GR; GRASE; Maintains highreductase 1 levels of reduced glutathione in the cytosol HIF1A Hypoxia-Transcription factor AKA MOP1; ARNT interacting inducible factorprotein; mediates the transcription 1, alpha subunit of oxygen regulatedgenes; induced by hypoxia HMOX1 Heme oxygenase Redox Enzyme AKA HO1;Essential for heme (decycling) 1 catabolism, cleaves heme to formbiliverdin and CO; endotoxin inducible ICAM1 Intercellular CellAdhesion/Matrix Endothelial cell surface molecule; adhesion Proteinregulates cell adhesion and molecule 1 trafficking, upregulated duringcytokine stimulation IGFBP3 Insulin-like AKA IBP3; Expressed by vasculargrowth factor endothelial cells; may influence binding protein 3insulin-like growth factor activity IL15 Interleukin 15cytokines-chemokines- Proinflammatory; mediates T-cell growth factorsactivation, inhibits apoptosis, synergizes with IL-2 to induce IFN-g andTNF-a IL1B Interleukin 1, cytokines-chemokines- Proinflammatory;constitutively beta growth factors and inducibly expressed by many celltypes, secreted IL8 Interleukin 8 cytokines-chemokines- Proinflammatory,major secondary growth factors inflammatory mediator, cell adhesion,signal transduction, cell- cell signaling, angiogenesis, synthesized bya wide variety of cell types MAPK1 mitogen- Transferase AKA ERK2; Maypromote entry activated protein into the cell cycle, growth factorkinase 1 responsive NFKB1 Nuclear Factor Transcription Factor AKA KBF1,EBP1; Transcription kappa B factor that regulates the expression ofinfolammatory and immune genes; central role in Cytokine inducedexpression of E-selectin NOS2A Nitric oxide Enzyme/Redox AKA iNOS;produces NO which is synthase 2A bacteriocidal/tumoricidal NOS3Endothelial Nitric AKA ENOS, CNOS; Synthesizes Oxide Synthase nitricoxide from oxygen and arginine; nitric oxide is implicated in vascularsmooth muscle relaxation, vascular endothelial growth factor inducedangiogenesis, and blood clotting through the activation of plateletsPLAT Plasminogen Protease AKA TPA; Converts plasminogin activator,tissue to plasmin; involved in fibrinolysis and cell migration PTGISProstaglandin I2 Isomerase AKA PGIS; PTGI; CYP8; (prostacyclin) CYP8A1;Converts prostaglandin synthase h2 to prostacyclin (vasodilator);cytochrome P450 family; imbalance of prostacyclin may contribute tomyocardial infarction, stroke, atherosclerosis PTGS2 Prostaglandin-Enzyme/Redox AKA COX2; Proinflammatory, endoperoxide member ofarachidonic acid to synthase 2 prostanoid conversion pathway; induced byproinflammatory cytokines PTX3 pentaxin-related AKA TSG-14; Pentaxin 3;Similar gene, rapidly to the pentaxin subclass of induced by IL-1inflammatory acute-phase proteins; beta novel marker of inflammatoryreactions SELE selectin E Cell Adhesion AKA ELAM; Expressed by(endothelial cytokine-stimulated endothelial adhesion cells; mediatesadhesion of molecule 1) neutrophils to the vascular lining SERPINE1Serine (or Proteinase Inhibitor AKA PAI1; Plasminogen activatorcysteine) protease inhibitor type 1; interacts with inhibitor, clade Btissue plasminogen activator to (ovalbumin), regulate fibrinolysismember 1 TEK tyrosine kinase, Transferase Receptor AKA TIE2, VMCM;Receptor for endothelial angiopoietin-1; may regulate endothelial cellproliferation and differentiation; involved in vascular morphogenesis;TEK defects are associated with venous malformations VCAM1 vascular cellCell Adhesion/Matrix AKA L1CAM; CD106; INCAM- adhesion Protein 100; Cellsurface adhesion molecule 1 molecule specific for blood leukocytes andsome tumor cells; mediates signal transduction; may be linked to thedevelopment of atherosclerosis, and rheumatoid arthritis VEGF VascularGrowth factor AKA VPF; Induces vascular Endothelial permeability andendothelial cell Growth Factor growth; associated with angiogenesis

TABLE 7 Cell Health and Apoptosis Gene Expression Panel Symbol NameClassification Description ABL1 V-abl Abelson oncogene Cytoplasmic andnuclear protein murine tyrosine kinase implicated in cell leukemia viraldifferentiation, division, adhesion oncogene and stress response.Alterations of homolog 1 ABL1 lead to malignant transformations. APAF1Apoptotic protease activator Cytochrome c binds to APAF1, Proteasetriggering activation of CASP3, Activating leading to apoptosis. Mayalso Factor 1 facilitate procaspase 9 autoactivation. BAD BCL2 Agonistmembrane protein Heterodimerizes with BCLX and of Cell Death countersits death repressor activity. This displaces BAX and restores itsapoptosis-inducing activity. BAK1 BCL2- membrane protein In the presenceof an appropriate antagonist/killer 1 stimulus BAK1 acceleratesprogrammed cell death by binding to, and antagonizing the repressor BCL2or its adenovirus homolog e1b 19k protein. BAX BCL2- membrane proteinAccelerates apoptosis by binding associated X to, and antagonizing BCL2or its protein adenovirus homolog e1b 19k protein. It induces therelease of cytochrome c and activation of CASP3 BCL2 B-cell membraneprotein Interferes with the activation of CLL/lymphoma 2 caspases bypreventing the release of cytochrome c, thus blocking apoptosis. BCL2L1BCL2-like 1 membrane protein Dominant regulator of apoptotic (long form)cell death. The long form displays cell death repressor activity,whereas the short isoform promotes apoptosis. BCL2L1 promotes cellsurvival by regulating the electrical and osmotic homeostasis ofmitochondria. BID BH3- Induces ice-like proteases and Interactingapoptosis. Counters the protective Death Domain effect of bcl-2 (bysimilarity). Agonist Encodes a novel death agonist that heterodimerizeswith either agonists (BAX) or antagonists (BCL2). BIK BCL2- Acceleratesapoptosis. Binding to Interacting the apoptosis repressors BCL2L1,Killer bhrfl, BCL2 or its adenovirus homolog e1b 19k protein suppressesthis death-promoting activity. BIRC2 Baculoviral apoptosis suppressorMay inhibit apoptosis by IAP Repeat- regulating signals required forContaining 2 activation of ICE-like proteases. Interacts with TRAF1 andTRAF2. Cytoplasmic BIRC3 Baculoviral apoptosis suppressor Apoptoticsuppressor. Interacts IAP Repeat- with TRAF1 and Containing 3TRAF2.Cytoplasmic BIRC5 Survivin apoptosis suppressor Inhibitsapoptosis. Inhibitor of CASP3 and CASP7. Cytoplasmic CASP1 Caspase 1proteinase Activates IL1B; stimulates apoptosis CASP3 Caspase 3proteinase Involved in activation cascade of caspases responsible forapoptosis- cleaves CASP6, CASP7, CASP9 CASP9 Caspase 9 proteinase Bindswith APAF1 to become activated; cleaves and activates CASP3 CCNA2 CyclinA2 cyclin Drives cell cycle at G1/S and G2/M phase; interacts with cdk2and cdc2 CCNB1 Cyclin B1 cyclin Drives cell cycle at G2/M phase;complexes with cdc2 to form mitosis promoting factor CCND1 Cyclin D1cyclin Controls cell cycle at G1/S (start) phase; interacts with cdk4and cdk6; has oncogene function CCND3 Cyclin D3 cyclin Drives cell cycleat G1/S phase; expression rises later in G1 and remains elevated in Sphase; interacts with cdk4 and cdk6 CCNE1 Cyclin E1 cyclin Drives cellcycle at G1/S transition; major downstream target of CCND1; cdk2-CCNE1activity required for centrosome duplication during S phase; interactswith RB cdk2 Cyclin- kinase Associated with cyclins A, D and dependentE; activity maximal during S phase kinase 2 and G2; CDK2 activation,through caspase-mediated cleavage of CDK inhibitors, may be instrumentalin the execution of apoptosis following caspase activation cdk4 Cyclin-kinase cdk4 and cyclin-D type complexes dependent are responsible forcell kinase 4 proliferation during G1; inhibited by CDKN2A (p16) CDKN1ACyclin- tumor suppressor May bind to and inhibit cyclin- Dependentdependent kinase activity, Kinase preventing phosphorylation ofInhibitor 1A critical cyclin-dependent kinase (p21) substrates andblocking cell cycle progression; activated by p53; tumor suppressorfunction CDKN2B Cyclin- tumor suppressor Interacts strongly with cdk4and Dependent cdk6; role in growth regulation but Kinase limited role astumor suppressor Inhibitor 2B (p15) CHEK1 Checkpoint, S. pombe Involvedin cell cycle arrest when DNA damage has occurred, or unligated DNA ispresent; prevents activation of the cdc2-cyclin b complex DAD1 Defendermembrane protein Loss of DAD1 protein triggers Against Cell apoptosisDeath DFFB DNA nuclease Induces DNA fragmentation and Fragmentationchromatin condensation during Factor, 40-KD, apoptosis; can be activatedby Beta Subunit CASP3 FADD Fas co-receptor Apoptotic adaptor moleculethat (TNFRSF6)- recruits caspase-8 or caspase-10 to associated via theactivated fas (cd95) or tnfr-1 death domain receptors; thisdeath-inducing signalling complex performs CASP8 proteolytic activationGADD45A Growth arrest regulator of DNA repair Stimulates DNA excisionrepair in and DNA vitro and inhibits entry of cells into damage S phase;binds PCNA inducible, alpha K-ALPHA-1 Alpha Tubulin, microtubule peptideMajor constituent of microtubules; ubiquitous binds 2 molecules of GTPMADD MAP-kinase co-receptor Associates with TNFR1 through a activatingdeath death domain-death domain domain interaction; Overexpression ofMADD activates the MAP kinase ERK2, and expression of the MADD deathdomain stimulates both the ERK2 and JNK1 MAP kinases and induces thephosphorylation of cytosolic phospholipase A2 MAP3K14 Mitogen- kinaseActivator of NFKB1 activated protein kinase kinase kinase 14 MRE11AMeiotic nuclease Exonuclease involved in DNA recombination double-strandbreaks repair (S. cerevisiae) 11 homolog A NFKB1 Nuclear factor nucleartranslational p105 is the precursor of the p50 of kappa light regulatorsubunit of the nuclear factor polypeptide NFKB, which binds to thekappa-b gene enhancer consensus sequence located in the in B-cells 1enhancer region of genes involved (p105) in immune response and acutephase reactions; the precursor does not bind DNA itself PDCD8 Programmedenzyme, reductase The principal mitochondrial factor Cell Death 8causing nuclear apoptosis. (apoptosis- Independent of caspase apoptosis.inducing factor) PNKP Polynucleotide phosphatase Catalyzes the 5-primekinase 3′- phosphorylation of nucleic acids phosphatase and can haveassociated 3-prime phosphatase activity, predictive of an importantfunction in DNA repair following ionizing radiation or oxidative damagePTEN Phosphatase tumor suppressor Tumor suppressor that modulates andtensin G1 cell cycle progression through homolog that negativelyregulating the PI3- (mutated in kinase/Akt signaling pathway; onemultiple critical target of this signaling advanced process is thecyclin-dependent cancers 1) kinase inhibitor p27 (CDKN1B). RAD52 RAD52(S. cerevisiae) DNA binding proteinsor Involved in DNA double-strandedhomolog break repair and meiotic/mitotic recombination RB1Retinoblastoma tumor suppressor Regulator of cell growth; interacts 1(including with E2F-like transcription factor; osteosarcoma) a nuclearphosphoprotein with DNA binding activity; interacts with histonedeacetylase to repress transcription SMAC Second mitochondrial peptidePromotes caspase activation in mitochondria- cytochrome c/APAF-1/caspase9 derived pathway of apoptosis activator of caspase TERT Telomerasetranscriptase Ribonucleoprotein which in vitro reverse recognizes asingle-stranded G-rich transcriptase telomere primer and adds multipletelomeric repeats to its 3-prime end by using an RNA template TNF Tumornecrosis cytokines-chemokines- Proinflammatory, TH1, mediates factorgrowth factors host response to bacterial stimulus, regulates cellgrowth & differentiation TNFRSF11A Tumor necrosis receptor ActivatesNFKB1; Important factor receptor regulator of interactions between Tsuperfamily, cells and dendritic cells member 11a, activator of NFKBTNFRSF12 Tumor necrosis receptor Induces apoptosis and activates factorreceptor NF-kappaB; contains a superfamily, cytoplasmic death domain andmember 12 transmembrane domains (translocating chain- associationmembrane protein) TOSO Regulator of receptor Potent inhibitor of Fasinduced Fas-induced apoptosis; expression of TOSO, apoptosis like thatof FAS and FASL, increases after T-cell activation, followed by adecline and susceptibility to apoptosis; hematopoietic cells expressingTOSO resist anti-FAS-, FADD-, and TNF-induced apoptosis withoutincreasing expression of the inhibitors of apoptosis BCL2 and BCLXL;cells expressing TOSO and activated by FAS have reduced CASP8 andincreased CFLAR expression, which inhibits CASP8 processing TP53 TumorProtein DNA binding protein- Activates expression of genes that 53 cellcycle-tumor inhibit tumor growth and/or suppressor invasion; involved incell cycle regulation (required for growth arrest at G1); inhibits cellgrowth through activation of cell-cycle arrest and apoptosis TRADDTNFRSF1A- co-receptor Overexpression of TRADD leads associated via to 2major TNF-induced responses, death domain apoptosis and activation ofNF- kappa-B TRAF1 TNF receptor- co-receptor Interact with cytoplasmicdomain associated of TNFR2 factor 1 TRAF2 TNF receptor- co-receptorInteract with cytoplasmic domain associated of TNFR2 factor 2 VDAC1Voltage- membrane protein Functions as a voltage-gated pore dependent ofthe outer mitochondrial anion channel 1 membrane; proapoptotic proteinsBAX and BAK accelerate the opening of VDAC allowing cytochrome c toenter, whereas the antiapoptotic protein BCL2L1 closes VDAC by bindingdirectly to it XRCC5 X-ray repair helicase Functions together with theDNA complementing ligase IV-XRCC4 complex in the defective repair repairof DNA double-strand in Chinese breaks hamster cells 5

TABLE 8 Cytokine Gene Expression Panel Symbol Name ClassificationDescription CSF3 Colony Cytokines/ AKA G-CSF; Cytokine that StimulatingChemokines/Growth stimulates granulocyte Factor 3 Factors development(Granulocyte) IFNG Interferon, Cytokines/ Pro- and anti-inflammatoyactivity; Gamma Chemokines/Growth TH1 cytokine; nonspecific Factorsinflammator mediator; produced by activated T-cells. Antiproliferativeeffects on transformed cells. IL1A Interleukin 1, Cytokines/Proinflammatory; constitutively Alpha Chemokines/Growth and induciblyexpressed in variety Factors of cells. Generally cytosolic and releasedonly during severe inflammatory disease IL1B Interleukin 1, Cytokines/Proinflammatory; constitutively Beta Chemokines/Growth and induciblyexpressed by many Factors cell types, secreted IL1RN Interleukin 1,Cytokines/ IL1 receptor antagonist; Receptor Chemokines/GrowthAntiinflammatory; inhibits binding Antagonist Factors of IL-1 to IL-1receptor by binding to receptor without stimulating IL- 1-like activityIL2 Interleukin 2 Cytokines/ T-cell growth factor, expressed byChemokines/Growth activated T-cells, regulates Factors lymphocyteactivation and a differentiation; inhibits apoptosis, TH1 cytokine IL4Interleukin 4 Cytokines/ Antiinflammatory; TH2; Chemokines/Growthsuppresses proinflammatory Factors cytokines, increases expression ofIL-1RN, regulates lymphocyte activation IL5 Interleukin 5 Cytokines/Eosinophil stimulatory factor; Chemokines/Growth stimulates late B cellFactors differentiation to secretion of Ig IL6 Interleukin 6 Cytokines/AKA Interferon, Beta 2; Pro- and Chemokines/Growth anti-inflammatoryactivity, TH₂ Factors cytokine, regulates hematopoiesis, activation ofinnate response, osteoclast development; elevated in sera of patientswith metastatic cancer IL10 Interleukin 10 Cytokines/ Antiinflammatory;TH₂; suppresses Chemokines/Growth production of proinflammatory Factorscytokines IL12\\BROMMAIN\ Interleukin 12 Cytokines/ Proinflammatory;mediator of VOL1\ (p40) Chemokines/Growth innate immunity, TH₁ cytokine,ALL Factors requires co-stimulation with IL-18 Primer Probe t induceIFN-γ TechSheets\Completed\IL!@ Btksht.doc IL13 Interleukin 13Cytokines/ Inhibits inflammatory cytokine Chemokines/Growth productionFactors IL15 Interleukin 15 Cytokines/ Proinflammatory; mediates T-cellChemokines/Growth activation inhibits apoptosis, Factors synergizes withIL-2 to induce IFN-γ and TNF-α IL18 Interleukin 18 Cytokines/Proinflammatory, TH1, innate and Chemokines/Growth acquired immunity,promotes Factors apoptosis, requires co-stimulation with IL-1 or IL-2 toinduce TH1 cytokines in T- and NK-cells IL18BP IL-18 Binding Cytokines/Implicated in inhibition of early Protein Chemokines/Growth TH1 cytokineresponses Factors TGFA Transforming Transferase/Signal Proinflammatorycytokine that is Growth Factor, Transduction the primary mediator ofimmune Alpha response and regulation, Associated with TH1 responses,mediates host response to bacterial stimuli, regulates cell growth &differentiation; Negative regulation of insulin action TGFB1Transforming Cytokines/ AKA DPD1, CED; Pro- and Growth Factors,Chemokines/Growth antiinflammatory activity; Anti- Beta 1 Factorapoptotic; cell-cell signaling, Can either inhibit or stimulate cellgrowth; Regulated by glucose in NIDDM individuals, overexpression (dueto oxidative stress promotes renal cell hypertrophy leading to diabeticnephropathy) TNFSF5 Tumor Necrosis Cytokines/ Ligand for CD40; Expressedon Factor (Ligand) Chemokines/Growth the surface of T-cells; RegulatesB- Superfamily, Factors cell function by engaging CD40 on Member 5 theB-cell surface TNFSF6 Tumor Necrosis Cytokines/Chemokines/ AKA FASL;Apoptosis antigen Factor (Ligand) Growth Factors ligand 1 is the ligandfor FAS Superfamily, antigen; Critical in triggering Member 6 apoptosisof some types of cells such as lymphocytes; Defects in protein may berelated to some cases of SLE TNFSF13B Tumor Necrosis Cytokines/ B-cellactivating factor, TNF Factor (Ligand) Chemokines/Growth familySuperfamily, Factors Member 13B

TABLE 9 TNF/IL1 Inhibition Gene Expression Panel HUGO Symbol NameClassification Description CD14 CD14 Antigen Cell Marker LPS receptorused as marker for monocytes GRO1 GRO1 Oncogene Cytokines/Chemokines/AKA SCYB1, Melanoma growth Growth Factors stimulating activity, Alpha;Chemotactic for neutrophils HMOX1 Heme Oxygenase Enzyme: Redox Enzymethat cleaves heme to form (Decycling) 1 biliverdin and CO; Endotoxininducible ICAM1 Intercellular Cell Adhesion: Matrix Endothelial cellsurface molecule; Adhesion Protein Regulates cell adhesion and Molecule1 trafficking: Up-regulated during cytokine stimulation IL1B Interleukin1, Cytokines/Chemokines/ Pro-inflammatory; Constitutively Beta GrowthFactors and inducibly expressed by many cell types; Secreted IL1RNInterleukin 1 Cytokines/Chemokines/ Anti-inflammatory; Inhibits ReceptorGrowth Factors binding of IL-1 to IL-1 receptor by Antagonist binding toreceptor without stimulating IL-1-like activity IL10 Interleukin 10Cytokines/Chemokines/ Anti-inflammatory; TH₂ cytokine; Growth FactorsSuppresses production of pro- inflammatory cytokines MMP9 MatrixProteinase/Proteinase AKA Gelatinase B; Degrades Metalloproteinase 9Inhibitor extracellular matrix molecules; Secreted by IL-8 stimulatedneutrophils SERPINE1 Serine (or Proteinase/Proteinase AKA Plasminogenactivator Cysteine) Inhibitor inhibitor-1, PAI-1; Regulator ofInhibitor, Clade E Protease fibrinolysis (Ovalbumin), Member 1 TGFB1Transforming Cytokines/Chemokines/ Pro- and anti-inflammatory GrowthFactor, Growth Factors activity; Anti-apoptotic; Cell-cell Beta 1signaling; Can either inhibit or stimulate cell growth TIMP1 TissueInhibitor Proteinase/Proteinase Irreversibly binds and inhibits ofInhibitor metalloproteinases such as Metalloproteinase 1 collagenaseTNFA Tumor Necrosis Cytokines/Chemokines/ Pro-inflammatory; TH₁cytokine; Factor, Alpha Growth Factors Mediates host response tobacterial stimulus; Regulates cell growth & differentiation

TABLE 10 Chemokine Gene Expression Panel Symbol Name ClassificationDescription CCR1 chemokine (C-C Chemokine receptor A member of the betachemokine motif) receptor 1 receptor family (seven transmembraneprotein). Binds SCYA3/MIP-1a, SCYA5/RANTES, MCP-3, HCC- 1, 2, and 4, andMPIF-1. Plays role in dendritic cell migration to inflammation sites andrecruitment of monocytes. CCR3 chemokine (C-C Chemokine receptor C-Ctype chemokine receptor motif) receptor 3 (Eotaxin receptor) binds toEotaxin, Eotaxin-3, MCP-3, MCP- 4, SCYA5/RANTES and mip-1 delta therebymediating intracellular calcium flux. Alternative co-receptor with CD4for HIV-1 infection. Involved in recruitment of eosinophils. Primarily aTh2 cell chemokine receptor. CCR5 chemokine (C-C Chemokine receptorMember of the beta chemokine motif) receptor 5 receptor family (seventransmembrane protein). Binds to SCYA3/MIP-1a and SCYA5/RANTES.Expressed by T cells and macrophages, and is an important co-receptorfor macrophage-tropic virus, including HIV, to enter host cells. Plays arole in Th1 cell migration. Defective alleles of this gene have beenassociated with the HIV infection resistance. CX3CR1 chemokine (C—X3—C)Chemokine receptor CX3CR1 is an HIV coreceptor as receptor 1 well as aleukocyte chemotactic/adhesion receptor for fractalkine. Natural killercells predominantly express CX3CR1 and respond to fractalkine in bothmigration and adhesion. CXCR4 chemokine (C—X—C Chemokine receptorReceptor for the CXC chemokine motif), receptor SDF1. Acts as aco-receptor with 4 (fusin) CD4 for lymphocyte-tropic HIV-1 viruses.Plays role in B cell, Th2 cell and naive T cell migration. GPR9 Gprotein- Chemokine receptor CXC chemokine receptor binds to coupledreceptor 9 SCYB10/IP-10, SCYB9/MIG, SCYB11/I-TAC. Binding of chemokinesto GPR9 results in integrin activation cytoskeletal changes andchemotactic migration. Prominently expressed in in vitro culturedeffector/memory T cells and plays a role in Th1 cell migration. GRO1GRO1 oncogene Chemokine AKA SCYB1; chemotactic for (melanomaneutrophils. GRO1 is also a growth mitogenic polypeptide secreted bystimulating human melanoma cells. activity, alpha) GRO2 GRO2 oncogeneChemokine AKA MIP2, SCYB2; Macrophage (MIP-2) inflammatory proteinproduced by monocytes and neutrophils. Belongs to intercrine familyalpha (CXC chemokine). IL8 interleukin 8 Chemokine Proinflammatory,major secondary inflammatory mediator, cell adhesion, signaltransduction, cell- cell signaling, angiogenesis, synthesized by a widevariety of cell types PF4 Platelet Factor 4 Chemokine PF4 is releasedduring platelet (SCYB4) aggregation and is chemotactic for neutrophilsand monocytes. PF4's major physiologic role appears to be neutralizationof heparin-like molecules on the endothelial surface of blood vessels,thereby inhibiting local antithrombin III activity and promotingcoagulation. SCYA2 small inducible Chemokine Recruits monocytes to areasof cytokine A2 injury and infection. Stimulates IL- (MCP1) 4 production;implicated in diseases involving monocyte, basophil infiltration oftissue (ie.g., psoriasis, rheumatoid arthritis, atherosclerosis). SCYA3small inducible Chemokine A “monokine” involved in the cytokine A3 acuteinflammatory state through (MIP1a) the recruitment and activation ofpolymorphonuclear leukocytes. A major HIV-suppressive factor produced byCD8-positive T cells. SCYA5 small inducible Chemokine Binds to CCR1,CCR3, and CCR5 cytokine A5 and is a chemoattractant for blood (RANTES)monocytes, memory t helper cells and eosinophils. A major HIV-suppressive factor produced by CD8-positive T cells. SCYB10 smallinducible Chemokine A CXC subfamily chemokine. cytokine Binding ofSCYB10 to receptor subfamily B CXCR3/GPR9 results in (Cys-X-Cys),stimulation of monocytes, natural member 10 killer and T-cell migration,and modulation of adhesion molecule expression. SCYB10 is induced byIFNg and may be a key mediator in IFNg response. SDF1 stromal cell-Chemokine Belongs to the CXC subfamily of derived factor 1 theintercrine family, which activate leukocytes. SDF1 is the primary ligandfor CXCR4, a coreceptor with CD4 for human immunodeficiency virus type 1(HIV-1). SDF1 is a highly efficacious lymphocyte chemoattractant.

TABLE 11 Breast Cancer Gene Expression Panel Symbol Name ClassificationDescription ACTB Actin, beta Cell Structure Actins are highly conservedproteins that are involved in cell motility, structure and integrity.ACTB is one of two non-muscle cytoskeletal actins. Site of action forcytochalasin B effects on cell motility. BCL2 B-cell membrane proteinInterferes with the activation of CLL/lymphoma 2 caspases by preventingthe release of cytochrome c, thus blocking apoptosis. CD19 CD19 antigenCell Marker AKA Leu 12; B cell growth factor CD34 CD34 antigen CellMarker AKA: hematopoietic progenitor cell antigen. Cell surface antigenselectively expressed on human hematopoietic progenitor cells.Endothelial marker. CD44 CD44 antigen Cell Marker Cell surface receptorfor hyaluronate. Probably involved in matrix adhesion, lymphocyteactivation and lymph node homing. DC13 DC13 protein unknown functionDSG1 Desmoglein 1 membrane protein Calcium-binding transmembraneglycoprotein involved in the interaction of plaque proteins andintermediate filaments mediating cell-cell adhesion. Interact withcadherins. EDR2 Early The specific function in human Development cellshas not yet been determined. Regulator 2 May be part of a complex thatmay regulate transcription during embryonic development. ERBB2 v-erb-b2Oncogene Oncogene. Overexpression of erythroblastic ERBB2 confers Taxolresistance in leukemia viral breast cancers. Belongs to the EGF oncogenetyrosine kinase receptor family. homolog 2 Binds gp130 subunit of theIL6 receptor in an IL6 dependent manner. An essential component of IL-6signalling through the MAP kinase pathway. ERBB3 v-erb-b2 OncogeneOncogene. Overexpressed in Erythroblastic mammary tumors. Belongs to theLeukemia Viral EGF tyrosine kinase receptor Oncogene family. Activatedthrough Homolog 3 neuregulin and ntak binding. ESR1 EstrogenReceptor/Transcription ESR1 is a ligand-activated Receptor 1 Factortranscription factor composed of several domains important for hormonebinding, DNA binding, and activation of transcription. FGF18 FibroblastGrowth Factor Involved in a variety of biological Growth Factorprocesses, including embryonic 18 development, cell growth,morphogenesis, tissue repair, tumor growth, and invasion. FLT1Fms-related Receptor Receptor for VEGF; involved in tyrosine kinase 1vascular development and regulation of vascular permeability. FOS V-fosFBJ Oncogene/ Leucine zipper protein that forms murine TranscriptionalActivator the transcription factor AP-1 by osteosarcoma dimerizing withJUN. Implicated viral oncogene in the processes of cell homologproliferation, differentiation, transformation, and apoptosis. GRO1 GRO1oncogene Chemokine/Growth Proinflammatory; chemotactic forFactor/Oncogene neutrophils. Growth regulator that modulates theexpression of metalloproteinase activity. IFNG Interferon, Cytokine Pro-and antiinflammatory activity; gamma TH1 cytokine; nonspecificinflammatory mediator; produced by activated T-cells. Antiproliferativeeffects on transformed cells. IRF5 Interferon Transcription FactorRegulates transcription of regulatory factor 5 interferon genes throughDNA sequence-specific binding. Diverse roles, include virus-mediatedactivation of interferon, and modulation of cell growth,differentiation, apoptosis, and immune system activity. KRT14 Keratin 14Cytoskeleton Type I keratin, intermediate filament component; KRT14 isdetected in the basal layer, with lower expression in more apicallayers, and is not present in the stratum corneum. Together with KRT5forms the cytoskeleton of epithelial cells. KRT19 Keratin 19Cytoskeleton Type I epidermal keratin; may form intermediate filaments.Expressed often in epithelial cells in culture and in some carcinomasKRT5 Keratin 5 Cytoskeleton Coexpressed with KRT14 to form cytoskeletonof epithelial cells. KRT5 expression is a hallmark of mitotically activekeratinocytes and is the primary structural component of the 10 nmintermediate filaments of the mitotic epidermal basal cells. MDM2 Mdm2,Oncogene/Transcription Inhibits p53- and p73-mediated transformed 3T3Factor cell cycle arrest and apoptosis by cell double binding itstranscriptional minute 2, p53 activation domain, resulting in bindingprotein tumorigenesis. Permits the nuclear export of p53 and targets itfor proteasome-mediated proteolysis. MMP9 Matrix Proteinase/ProteinaseDegrades extracellular matrix by metalloproteinase 9 Inhibitor cleavingtypes IV and V collagen. Implicated in arthritis and metastasis. MP1Metalloprotease 1 Proteinase/Proteinase Member of the pitrilysin family.A Inhibitor metalloendoprotease. Could play a broad role in generalcellular regulation. N33 Putative prostate Tumor Suppressor Integralmembrane protein. cancer tumor Associated with homozygous suppressordeletion in metastatic prostate cancer. OXCT 3-oxoacid CoA TransferaseOXCT catalyzes the reversible transferase transfer of coenzyme A fromsuccinyl-CoA to acetoacetate as the first step of ketolysis (ketone bodyutilization) in extrahepatic tissues. PCTK1 PCTAIRE Belongs to theSER/THR family of protein kinase 1 protein kinases; CDC2/CDKX subfamily.May play a role in signal transduction cascades in terminallydifferentiated cells. SERPINB5 Serine proteinase Proteinase/ProteinaseProtease Inhibitor; Tumor inhibitor, clade Inhibitor/Tumor suppressor,especially for B, member 5 Suppressor metastasis. Inhibits tumorinvasion by inhibiting cell motility. SRP19 Signal Responsible forsignal-recognition- recognition particle assembly. SRP mediates particle19 kD the targeting of proteins to the endoplasmic reticulum. STAT1Signal transducer DNA Binding Protein Binds to the IFN-Stimulated andactivator of Response Element (ISRE) and to transcription 1, the GASelement; specifically 91 kD required for interferon signaling. STAT1 canbe activated by IFN- alpha, IFN-gamma, EGF, PDGF and IL6.BRCA1-regulated genes overexpressed in breast tumorigenesis includedSTAT1 and JAK1. TGFB3 Transforming Cell Signalling Transmits signalsthrough growth factor, transmembrane serine/threonine beta 3 kinases.Increased expression of TGFB3 may contribute to the growth of tumors.TLX3 T-cell leukemia, Transcription Factor Member of the homeodomainhomeobox 3 family of DNA binding proteins. May be activated in T-ALLleukomogenesis. VWF Von Willebrand Coagulation Factor Multimeric plasmaglycoprotein factor active in the blood coagulation system as anantihemophilic factor (VIIIC) carrier and platelet-vessel wall mediator.Secreted by endothelial cells.

TABLE 12 Infectious Disease Gene Expression Panel Symbol NameClassification Description C1QA Complement Proteinase/Proteinase Serumcomplement system; forms component 1, q Inhibitor C1 complex with theproenzymes subcomponent, clr and cls alpha polypeptide CASP1 Caspase 1proteinase Activates IL1B; stimulates apoptosis CD14 CD14 antigen CellMarker LPS receptor used as marker for monocytes CSF2 Granulocyte-cytokines-chemokines- AKA GM-CSF; Hematopoietic monocyte colony growthfactors growth factor; stimulates growth stimulating factor anddifferentiation of hematopoietic precursor cells from various lineages,including granulocytes, macrophages, eosinophils, and erythrocytes EGR1Early growth cell signaling and master inflammatory switch forresponse-1 activation ischemia-related responses including chemokinesysntheis, adhesion moelcules and macrophage differentiation F3 F3Enzyme/Redox AKA thromboplastin, Coagulation Factor 3; cell surfaceglycoprotein responsible for coagulation catalysis GRO2 GRO2 oncogenecytokines-chemokines- AKA MIP2, SCYB2; Macrophage growth factorsinflammatory protein produced by monocytes and neutrophils HMOX1 Hemeoxygenase Enzyme/Redox Endotoxin inducible (decycling) 1 HSPA1A Heatshock Cell Signaling and heat shock protein 70 kDa protein 70 activationICAM1 Intercellular Cell Adhesion/Matrix Endothelial cell surfacemolecule; adhesion Protein regulates cell adhesion and molecule 1trafficking, upregulated during cytokine stimulation IFI16 gamma cellsignaling and Transcriptional repressor interferon activation inducibleprotein 16 IFNG Interferon cytokines-chemokines- Pro- andantiinflammatory activity, gamma growth factors TH1 cytokine,nonspecific inflammatory mediator, produced by activated T-cells IL10Interleukin 10 cytokines-chemokines- Antiinflammatory; TH2; growthfactors suppresses production of proinflammatory cytokines IL12BInterleukin 12 cytokines-chemokines- Proinflammatory; mediator of p40growth factors innate immunity, TH1 cytokine, requires co-stimulationwith IL-18 to induce IFN-g IL13 Interleukin 13 cytokines-chemokines-Inhibits inflammatory cytokine growth factors production IL18Interleukin 18 cytokines-chemokines- Proinflammatory, TH1, innate andgrowth factors aquired immunity, promotes apoptosis, requiresco-stimulation with IL-1 or IL-2 to induce TH1 cytokines in T- andNK-cells IL18BP IL-18 Binding cytokines-chemokines- Implicated ininhibition of early Protein growth factors TH1 cytokine responses IL1AInterleukin 1, cytokines-chemokines- Proinflammatory; constitutivelyalpha growth factors and inducibly expressed in variety of cells.Generally cytosolic and released only during severe inflammatory diseaseIL1B Interleukin 1, cytokines-chemokines- Proinflammatory;constitutively beta growth factors and inducibly expressed by many celltypes, secreted IL1R1 interleukin 1 receptor AKA: CD12 or IL1R1RAreceptor, type I IL1RN Interleukin 1 cytokines-chemokines- IL1 receptorantagonist; receptor growth factors Antiinflammatory; inhibits bindingantagonist of IL-1 to IL-1 receptor by binding to receptor withoutstimulating IL- 1-like activity IL2 Interleukin 2 cytokines-chemokines-T-cell growth factor, expressed by growth factors activated T-cells,regulates lymphocyte activation and differentiation; inhibits apoptosis,TH1 cytokine IL4 Interleukin 4 cytokines-chemokines- Antiinflammatory;TH2; growth factors suppresses proinflammatory cytokines, increasesexpression of IL-1RN, regulates lymphocyte activation IL6 Interleukin 6cytokines-chemokines- Pro- and antiinflammatory activity, (interferon,beta growth factors TH2 cytokine, regulates 2) hemotopoietic system andactivation of innate response IL8 Interleukin 8 cytokines-chemokines-Proinflammatory, major secondary growth factors inflammatory mediator,cell adhesion, signal transduction, cell- cell signaling, angiogenesis,synthesized by a wide variety of cell types MMP3 MatrixProteinase/Proteinase AKA stromelysin; degrades metalloproteinase 3Inhibitor fibronectin, laminin and gelatin MMP9 MatrixProteinase/Proteinase AKA gelatinase B; degrades metalloproteinase 9Inhibitor extracellular matrix molecules, secreted by IL-8-stimulatedneutrophils PLA2G7 Phospholipase Enzyme/Redox Platelet activating factorA2, group VII (platelet activating factor acetylhydrolase, plasma) PLAUPlasminogen Proteinase/Proteinase AKA uPA; cleaves plasminogen toactivator, Inhibitor plasmin (a protease responsible for urokinasenonspecific extracellular matrix degradation) SERPINE1 Serine (orProteinase/Proteinase Plasminogen activator inhibitor-1/ cysteine)Inhibitor PAI-1 protease inhibitor, clade B (ovalbumin), member 1 SOD2superoxide Oxidoreductase Enzyme that scavenges and dismutase 2,destroys free radicals within mitochondrial mitochondria TACI Tumornecrosis cytokines-chemokines- T cell activating factor and calciumfactor receptor growth factors cyclophilin modulator superfamily, member13b TIMP1 tissue inhibitor of Proteinase/Proteinase Irreversibly bindsand inhibits metalloproteinase 1 Inhibitor metalloproteinases, such ascollagenase TLR2 toll-like receptor 2 cell signaling and mediator ofpetidoglycan and activation lipotechoic acid induced signalling TLR4toll-like receptor 4 cell signaling and mediator of LPS inducedsignalling activation TNF Tumor necrosis cytokines-chemokines-Proinflammatory, TH1, mediates factor, alpha growth factors hostresponse to bacterial stimulus, regulates cell growth & differentiationTNFSF13B Tumor necrosis cytokines-chemokines- B cell activating factor,TNF factor (ligand) growth factors family superfamily, member 13b TNFSF5Tumor necrosis cytokines-chemokines- ligand for CD40; expressed on thefactor (ligand) growth factor surface of T cells. It regulates Bsuperfamily, cell function by engaging CD40 on member 5 the B cellsurface TNFSF6 Tumor necrosis cytokines-chemokines- AKA FasL; Ligand forFAS factor (ligand) growth factors antigen; transduces apoptoticsuperfamily, signals into cells member 6 VEGF vascularcytokines-chemokines- Producted by monocytes endothelial growth factorsgrowth factor IL5 Interleukin 5 Cytokines-chemokines- Eosinophilstimulatory factor; growth factors stimulates late B celldifferentiation to secretion of Ig IFNA2 Interferon alpha 2Cytokines-chemokines- interferon produced by growth factors macrophageswith antiviral effects TREM1 TREM-1 Triggering Receptor Receptor/CellSignaling and Expressed on Myeloid Activation Cells 1 SCYB10 smallinducible Chemokine A CXC subfamily chemokine. cytokine Binding ofSCYB10 to receptor subfamily B CXCR3/GPR9 results in (Cys-X-Cys),stimulation of monocytes, natural member 10 killer and T-cell migration,and modulation of adhesion molecule expression. SCYB10 is Induced byIFNg and may be a key mediator in IFNg response. CCR1 Chemokine (C-CChemokine receptor A member of the beta chemokine motif) receptor 1receptor family (seven transmembrane protein). Binds SCYA3/MIP-1a,SCYA5/RANTES, MCP-3, HCC- 1, 2, and 4, and MPIF-1. Plays role indendritic cell migration to inflammation sites and recruitment ofmonocytes. CCR3 Chemokine (C-C Chemokine receptor C-C type chemokinereceptor motif) receptor 3 (Eotaxin receptor) binds to Eotaxin,Eotaxin-3, MCP-3, MCP- 4, SCYA5/RANTES and mip-1 delta thereby mediatingintracellular calcium flux. Alternative co-receptor with CD4 for HIV-1infection. Involved in recruitment of eosinophils. Primarily a Th2 cellchemokine receptor. SCYA3 Small inducile Chemokine A “monokine” involvedin the cytokine A3 acute inflammatory state through (MIP1a) therecruitment and activation of polymorphonuclear leukocytes. A majorHIV-suppressive factor produced b CD8-positive T cells. CX3CR1 Chemokine(C—X3—C) Chemokine receptor CX3CR1 is an HIV coreceptor as receptor 1well as a leukocyte chemotactic/adhesion receptor for fractalkine.Natural killer cells predominantly express CX3CR1 and respond tofractalkine in both migration and adhesion.

1-8. (canceled)
 9. A method of predicting efficacy of an agent on abiological condition, the method comprising: a) producing a first indexfor a sample from a subject, the sample providing a source of RNAscomprising: i) using amplification for quantitatively measuring theamount of RNA from the sample from the subject, wherein a panel ofconstituents is selected so that measurement conditions of theconstituents enables evaluation of said biological condition and whereinthe measures of all constituents in the panel form a first profile dataset, wherein said amplification for each constituent is under conditionsthat are (1) within a degree of repeatability of better than fivepercent; and (2) the efficiencies of amplification are within twopercent; ii) using amplification for quantitatively measuring the amountof RNA of all constituents in said panel wherein the measures of allconstituents in the panel are from a relevant population of subjects andform a normative baseline profile data set; and iii) applying valuesfrom the first profile data set to an index function derived usinglatent class modeling, thereby providing a single-valued measure of thebiological condition so as to produce an index pertinent to thebiological condition of the subject; wherein the index function alsouses data from the normative baseline profile data set, b) index topredict the efficacy of the agent on the biological condition of thesubject.
 10. The method of claim 9, wherein the agent is a therapeuticagent.
 11. The method of claim 10, wherein the agent is achemotherapeutic agent.
 12. The method of claim 9, wherein thebiological condition is a cancer.
 13. The method of claim 9, wherein thebiological condition is an infection
 14. The method of claim 9, whereintreatment of the biological condition is based on the prediction ofefficacy of the agent.
 15. A method of evaluating the efficacy of anagent, the method comprising (a) producing an index according to claim9; and (b) using the index to evaluate the efficacy of the agent.